Reviewerly/exHarmony huggingface dataset:
Preparing Future Physics Teachers To Work In A Virtual Learning Environment

Digital transformation of education poses new challenges in the preparation of future teachers, including teachers of physics. The basis of digital competence of teachers is digital skills, which are manifested in particular in the ability to apply electronic educational resources: training programs, simulators, computer models, etc., as well as a set of such tools - virtual learning environments. The preparation of a future physics teacher for the management of students cognitive activity combines the work of a teacher at the same time in two directions. The first direction is the development by students of the features of the management process of the cognitive activity of schoolchildren. The second direction is the teaching students how to present the elements of content and how to master them in digital form by using ready-made or independently developed electronic educational resources. The implementation of productive technology by the teacher helps to improve the efficiency of preparing the future physics teacher for the management of the cognitive activity of students using digital educational resources. As a result, students embody their knowledge and skills in an educational product. This is a set of teaching and learning materials that is objectively new and significant for their future professional activity. The author of the article presents the experience of developing the content of training future physics teachers to manage learning and cognitive activity in a virtual learning environment which is based on modern ideas about the development of education.
Machine Learning Prediction of Wikipedia Time Series Data using: R Programming

his review article explains the prediction of automatic learning of Wikipedia time series data using r programming. Although many time series forecast researchers have been analyzed the time series could not cover the gap between chart interpretation and time series analysis of the Internet database directly. Its main objective is to explain the simplest way to time model series whose data structure was different using R programming, the result was sufficiently summarized with different forecast models. The simplest form of analysis with graphical interpretation to obtain conclusions from the time search Cristiano_Ronaldo of Wikipedia, a best player in euro football team. Whose trend and prediction is analyzed for next 2020 from the past records trend. Therefore, this document presents the simplest way to predict time series data and its strengths for data analysis using R programming.
Computational Linguistics: Introduction to the Thematic Issue

This special issue of Computación y Sistemaspresents a selection of papers on natural language processing and computational linguistics, along with two regular papers. Natural language processing is an area of artificial intelligence and its applications devoted to analysis and generation of data streams involved in human communication using language, such as English or Spanish, typically in the form of text or speech, as well as, in multimodal setting, associated facial expressions and body language. It embraces natural language processing and computational linguistics in a wider context of their real-life applications in a wide range of disciplines.
Development of Feature Descriptor for Texture Classification

Image classification strategies based on surfaces play an imperative part in different computer vision applications. The challenge exists in texture classification are rotation, illumination, scale and orientation changes. Our proposed method uses efficient features for texture classification that overcomes all the above challenges. The steps involved in this algorithm are: The basis filters were calculated in multiple scales, by using the first and second-order Gaussian directional derivatives. These basis filters are convolved with input image to compute max-min responses. By applying linear and non-linear administrators on max-min reactions, the highlights have been extricated. The 2D-DWT is applied to the same input image. The Local Binary Pattern features were extracted for the sub-bands of 2D-DWT applied image. Scalar quantization is grasped to quantize these texture features into surface codes, as these feature set are high-dimensional which are unseemly to concatenate all highlights over a entirety picture. The cross - scale coding has been performed over these quantized codes for a compact representation of the histogram. This compact histogram representation was used as an image feature representation for the classification process. For classification KNN classifier is used. The performance of the algorithm is validated by using OUTEX-10 and OUTEX-14 datasets.
Practical, Dynamic and Efficient Integrity Verification for Symmetric Searchable Encryption

Symmetric searchable encryption (SSE) has been proposed for years and widely used in cloud storage. It enables individuals and enterprises to outsource their encrypted personal data to cloud server and achieves efficient search. Currently most SSE schemes are working in the semi-honest or curious cloud server model in which the search results are not absolutely trustworthy. Thus, the verifiable SSE (VSSE) schemes are proposed to enable data integrity verification. However, the majority of existing VSSE schemes have their own limitations, such as not supporting dynamics (data updates), working in single-user mode or not practical etc. In this paper, we propose a practical, dynamic and efficient integrity verification method for SSE construction that is decoupled from original SSE schemes. This paper proposed a practical and general SSE (PGSSE for short) scheme to achieve more efficient data integrity verification in comparison with the state-of-the-art schemes. The proposed PGSSE can be applied to any top-k ranked SSE scheme to achieve integrity verification and efficient data updates. Security analysis and experimental results demonstrate that the proposed PGSSE scheme is secure and efficient.
Methodology for assessing end-user requirements in the Ella4Life project: elders’ perspectives about self-monitoring

The purpose of this paper is to explore elders' perspectives about self-monitoring and using specially developed sensor technology for measuring health indicators. The qualitative research method is focus-groups with guidelines that were designed for understanding elder's requirements about monitoring health indicators. We present them two devices: the first sensor is a device for monitoring of cardiac action potential fixed into an armchair, the second sensor for monitoring vital signs is placed in a bathtub. The people express their needs and expectations regarding the idea of having sensors for monitoring health indicators and body movement in their own house.
Optimal Stealth Trajectory Design to Deceive Anomaly Detection Process

A new methodology is proposed to deceive an anomalous trajectory detector by designing ship paths that deviate from the nominal traffic routes in an optimized way. The route planning is formalized as a min-max problem (with respect to surveillance system acquisition instants) focusing on the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. Modeling the vessel’s dynamic according to the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process, physical, practical, and kinematic requirements are also accounted for forcing several constraints at the design stage. A computationally efficient technique is proposed to handle the resulting non-convex optimization problem, and some case studies are reported to assess its effectiveness.
Linear capabilities for CHERI: an exploration of the design space

CHERI is an instruction set extension that adds capability-based addressing. With capability-based addressing, forgeable pointers are replaced by capabilities. Programs have to be able to show they possess an appropriate capability before they can access a memory location. Linear capabilities are an extension of the idea of regular capabilities. Just like regular capabilities they cannot be forged. Unlike regular capabilities, they additionally cannot be copied or duplicated in any way. Linear capabilities allow us to enforce certain security principles. They can be used to implement a secure calling convention called StkTokens. In this work we will be designing an ISA extension for CHERI so it can support linear capabilities. While doing this, we pose several questions one should consider when designing an extension of this kind, and propose some possible solutions.
SSD Net: toward deep network models based on dissimilarity metrics

Most artificial neural networks that originate from the perceptron use the inner product as the basic operation to calculate pattern similarities. Unlike them, we propose a novel hierarchical network model based on a pattern dissimilarity operation using a popular dissimilarity metric: sum of squared differences. Our model is differentiable and end-to-end trainable. We provide a description of the basic formulation and network architecture of the proposed method. Then we apply our method to image classification tasks using public datasets for performance comparison. Although our method does not outperform the same size of convolutional neural network in terms of classification accuracy, it demonstrates that comparable performance can be obtained.
A Residue Number System Based Data Hiding Using Steganography and Cryptography

Cryptography and Steganography are two distinct approaches for protected data hiding and diffusion that are widely obtainable.  One hides the presence of the message and the other garbles the message.  The practices made use of information to cipher or cover their existence correspondingly. Cryptography is the science of using mathematics to encrypt and decrypt data; the data are transformed into some other gibberish form, and then the encrypted data are diffused. While Steganography is the art and science of hiding messages, steganography embeds hidden content in an unexceptional cover media to avoid spy’s suspicion. In steganography the clandestine message embeds in an undisruptive looking cover such as a digital image file and the image file is transmitted.  In our proposed method, we use Steganography procedure where secret message is implanted within a hauler image file and the existence of message is hidden from the prowler. To prevent disclosure of contents of the covered file RSA algorithm is used with Steganography to enhance the sturdiness of the system. Usually in practice RSA public and private exponents are chosen to be very large this makes the decryption process slow. And to speed it up we employ the use of Chinese Remainder Theorem which concentrates on modulus calculation. This paper proposes a faster RSA-CRT algorithm for decryption of data. And by employing this technique on RSA algorithm by matching data to an image, there is less chance of an attacker being able to use steganalysis to recover data. Before hiding the data in an image, the application first encrypts it.Keywords: RSA algorithm, cryptography, steganography, LSB method, Chinese Remainder Theorem
Implementation of Filter Picker Algorithm For Aftershock Identification of Lombok Earthquake 2018

The ability to identify earthquake events that are consistent, efficient and accurate is increasingly needed along with the increase in the amount of data analyzed. In this paper a filter picker algorithm is implemented to identify aftershock events and determination of arrival time automatically, especially for the P wave phase. Here modifications are made in determining the uncertainty of arrival time and there are additional criteria in determining the time of arrival used. The additional criteria are that in a certain time span, there are at least 5 stations determined by the time the filter picker arrives. This is done to minimize identification errors due to local noise and other practical reasons, namely the minimum number of stations to determine the location and other seismological analysis. To test the filter picker algorithm, aftershock data from the Lombok earthquake occurred on July 29 (M 6.4), August 5 (M 7), and August 19 (M 6.3 and M 6.9) 2018. The aftershock data were used for 30 days, from August 4, 2018 to September 4, 2018 using local seismic station in Lombok Island. The results of the filter picker algorithm were evaluated by comparing the number of earthquake events detected and the accuracy of determining the P wave arrival time automatically to the results of manually arriving time. In addition, a comparison of the results obtained from a broadband type seismometer with a short period is used to find out how much influence the type of tool has on its performance results. The results of the comparison with the manual arrival time show that more than 85 percent of the results of the automatic arrival time have a difference below 0.2 seconds. Therefore, it can be said that the filter picker algorithm is quite effective for identifying events and determining the arrival time of P waves. In this paper it is also shown that this algorithm can be used for broad band and short period seismometer sensor, even without the prior correction of instruments.
Evidential reasoning with linguistic belief structures

The Evidential Reasoning (ER) approach is an inference method to analyse multi-criteria decision-making problems with uncertainties. However, due to the pressure of time, limit of knowledge and lack of data, such problems need to be assessed using both human judgments and limited data. In this case, it is suitable to provide a belief structure by means of linguistic terms, which we refer to as linguistic belief structure. Considering that different decision-makers may have different perceptions, the semantics of human judgments can also be different. In this paper, we introduce three linguistic scale functions to capture the semantics of linguistic belief degrees for constructing a complete linguistic information fusion method. We then extend the framework of the ER approach to multi-expert multi-criteria decision making. The method proposed in this study can fill the research gap of the ER approach for multi-expert multi-criteria decision-making problems with linguistic belief structures.
Expert System Technology: Expert System Interface

This chapter aims to identify reasons for the relative weakness of the link and clarify issues that will be essential to the strengthening of such a link and thereby increase the usability of expert systems as a whole. The knowledge of several human experts can be combined to give a system more breadth than a single person is likely to achieve. Human experts automatically adapt to changing environments; expert systems must be explicitly updated. Expert systems raise new design issues concerning three separate but highly interacting aspects of a design process: knowledge capture, encoding the knowledge and expertise into the system, and ensuring that the system is accepted by an active user community. Intelligent interfaces that are based on expert systems technology will either need enhancements to their user model or else the use input will be constrained to be rather specific to avoid the problems of ambiguity in language.
Fully On-Chip Single-Photon Hanbury-Brown and Twiss Experiment on a Monolithic Semiconductor-Superconductor Platform

Currently, several aspects of quantum technology are deeply investigated, from quantum metrology and sensing to quantum computation and simulation. These last implementations, in particular, will greatly benefit from the use of integrated photonics in spite of large table top setups. With the purpose of keep increasing the experimental complexity, scalability indeed becomes an important requirement.
Application of Data Mining for Optimal Drug Inventory in a Hospital

The Hospital is a health care institution that conducts complete individual health services that provide inpatient, outpatient and emergency services. Drug inventory management is one thing that is very important for the survival of hospitals, management of the supply of medical equipment that is not optimal including medicines will have an impact on medical services as well as economically, because 70% of hospital revenue comes from drugs. In this study we propose data mining with a focus on contributions to the comparison of the K-Means and K-Nearest Neighbor (KNN) algorithms for disease classification, then the classification results are carried out mapping the correlation of diseases with drugs using Apriori, based on the results of testing the K-Means algorithm more accurately compared KNN in the Apriori method to find the relationship of disease with drugs based on the value of support, trust, support value, trust is expected to be a reference for drug purchase recommendations so that there is no excess or emptiness of the drug.
Secure Incident Handling

When masquerader tries to cheat online user by applying a mixture of tricks for luring the user to share personal information, it results in a phishing attack. Phishing is very crucial kind of attack which usually happens through social sites or frequently visited websites by the user. Incident or event handling is similar to emergency medicine. The caregiver tends to be under pressure, and mistakes can be very costly. After the occurrence of incidence, users have to face other information robberies. Secure Incident Handling is the need of tomorrow’s wireless cyber world. This chapter elaborates different cyber security aspects for Incident Handling and Phishing.
New Analysis of Reduced-Version of Piccolo in the Single-Key Scenario

The lightweight block cipher Piccolo adopts Generalized Feistel Network structure with 64 bits of block size.Its key supports 80 bits or 128 bits, expressed by Piccolo-80 or Piccolo-128, respectively.In this paper, we exploit the security of reduced version of Piccolo from the first round with the pre-whitening layer, which shows the vulnerability of original Piccolo.As a matter of fact, we first study some linear relations among the round subkeys and the properties of linear layer.Based on them, we evaluate the security of Piccolo-80/128 against the meet-in-the-middle attack.Finally, we attack 13 rounds of Piccolo-80 by applying a 5-round distinguisher, which requires 2 44 chosen plaintexts, 2 67.39 encryptions and 2 64.91 blocks, respectively.Moreover, we also attack 17 rounds of Piccolo-128 by using a 7-round distinguisher, which requires 2 44 chosen plaintexts, 2 126 encryptions and 2 125.49blocks, respectively.Compared with the previous cryptanalytic results, our results are the currently best ones if considering Piccolo from the first round with the pre-whitening layer.
Research on Aero-engine Vibration Fault Based on Statistical Feature and Artificial Intelligence Method

The vibration signals of the aero-engine compose of multi-frequency components as well as an abundant of noise. The signal was decomposed based on wavelet transform, the statistical feature of generalized roughness vector of the signal was formed by making use of the local energy distributions and the roughness of the sub-band signal. Then the desired parameters serve as the fault characteristic vectors to be input into two neural networks classifiers respectively and the fault patterns were identified by the output of the classifiers. These results were fused by applying the Dempster-Shafer (D-S) evidential theory of the information fusion. The analysis results from the aero-engine vibration signals show that the fault diagnosis method can classify working conditions and fault patterns effectively.
A watermarking model for omni-directional digital images

In this contribution, a watermarking system for omni-directional images is presented. To cope with the peculiarities of this media, an ad-hoc watermarking technique has been designed. In particular, since the users can only view a small portion of the 360° images, the viewport, the watermarking procedure has been designed for allowing the extraction of the watermark even from a single viewport. In this way it is possible to protect the ownership of the content even if only a viewport is illegally distributed. The experimental results show that the proposed system is robust against several kinds of attacks, outperforming the current state-of-the-art in omni-directional image watermarking.
Lattice-based Fuzzy Conditional Proxy Re-encryption

As an extension to conditional proxy re-encryption, fuzzy conditional proxy re-encryption allows for a proxy to re-encrypt a ciphertext only if it satisfies some t-out-of-d threshold condition. Therefore, it is more desirable in some applications where fine-grained control of the decryption delegation is required. In this paper, we construct the first lattice-based fuzzy conditional proxy re-encryption scheme as a post-quantum alternative to this primitive. In our construction, original ciphertexts and re-encryption ciphertexts have the same form, thus only one decryption algorithm is needed for both kinds of ciphertexts. We formalize its security model and prove that it is selective secure under the LWE assumption.
A High-performance Computing Method for Photographic Mosaics upon the Hadoop Framework

Since digital images are non-structure data, in order to apply some image processing techniques on digital images, we need high computing power. Nowadays, digital images have become an issue of Big Data, so we decide to implement an adaptive K-Medoids based on a popular Big Data analysis tool, Hadoop, in our research. Hadoop can provide high computing power to do processes of high computational complexity required by the algorithm of mosaic images, especially, in a Big Data environment where there are tons of images to be dealt. Our research focuses on three main goals. First, we use an unsupervised clustering method, K-Medoids, to cluster the image dataset and build a codebook, and then we can use the codebook to generate the mosaic image to reduce the processing time. Second, we use two feature selection metrics to develop an adaptive K-Medoids method, called feature-based K-Medoids (FKM), which can cluster the image dataset faster by the feature selection mechanism. Third, our method surely reduces the processing time of mosaic images by the codebook. Though the image quality by our method is slightly lower compared with Szul et al.’s method, our method retains an acceptable image quality.
ANALISIS KOMPARASI ALGORITMA KLASIFIKASI DATA MINING UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA AKADEMI BINA SARANA INFORMATIKA

The role of data mining in solving a large data problem includes estimation, prediction, classification, clustering and association. One of the roles of data mining used in this study is the classification to predict the graduation of Bina Sarana Informatika students in 2017 with the provision of passing on time and passing late. The algorithm used is the Decision Tree, K-Nerest Neighbor, Naive Bayes, Random tree and Random Forest. The number of students graduating in the 1st period of 2017 was 5870 where there were 4998 students graduating on time and 872 graduating late. Performance testing of several algorithms using validation and different tests T-Test The k-NN algorithm has a high accuracy value of 99.98% which is the highest value and the best AUC is obtained by the Naive Bayes algorithm with a value of 1,000. While the difference test is t-test significance 0.05 then H0 is accepted, only Decision Three against Naive Bayes is accepted, otherwise it is rejected.