Reviewerly/exHarmony huggingface dataset:
Subject Review: Cloud Computing Security Based on Cryptography

The cloud computing providing to users and companies many services and facilities such as less cost, availability and risk management. The services of cloud computing are delivered in pay per usage way and on-demand and it can store huge amount of data. But the security concern is the main reason that makes companies afraid of using cloud computing and store their data on cloud servers. In this paper, we try to review and describe many encryption methods that used to protect data which stored in cloud computing and make a comparison among these methods.
CATEGORIZATION OF IMPACT OF THE SELECTED VARIABLES FOR POTENTIAL BROWNFIELD REGENERATION IN THE CZECH REPUBLIC BY MEANS OF CORRESPONDENCE ANALYSIS

Brownfields are undesirable urbanistic heritage and their occurrence is associated with a number of negative effects, which our society tries to minimize repeatedly, both successfully and unsuccessfully.Especially in the context of the multi-perspective concept of sustainable development, the issue of brownfields is accentuated with the intention to reuse abandoned areas that have lost their original use and regenerate them in a way that enriches not only individual stakeholders but generally the whole society.Although each brownfield is unique within its set of partial characteristics, it is possible to determine by means of mathematical-statistical methods the key patterns that are important for brownfields in terms of their overall nature.The identification of these common patterns across a selected set of brownfields is important for establishing of general recommendations for their potential regeneration.In our paper, we proceeded from the analysis of 460 non-regenerated brownfields located in the Czech Republic, and by means of correspondence analysis we determined which categories most influence the potential brownfield regeneration from the perspective of a two-dimensional (stage) approach.Information on brownfields was taken from the publicly available database of the state-funded company the CzechInvest for 2018 and supplemented by authors with other relevant variables.The analysis of the data revealed factors that may have a major impact in the initial phase of the decision on potential brownfield regeneration, and at the same time factors that do not seem to be substantial for regeneration.The applicability of the results of our analyses creates space for better-targeted brownfield regeneration policies, especially in the area of specifications for calls for brownfield regeneration projects, their financial support, implementation of process innovations, etc.
BAHAN AJAR PPKn BERBASIS KARAKTER DAN LITERASI UNTUK SISWA KELAS IX SMP AL HIKMAH SURABAYA

This study aims to produce teaching materials based on Character and LiteracyPPKn Class IX students who are designed to be more interesting, to train independently, creatively and innovatively, according to students 'needs and develop students' competence, character and literacy.The development model used by Dick, Carey & Carey (2001) adjusted to the development needs.This analysis technique is used to process data obtained through evaluation sheets in the form of descriptive percentages.The validation results from learning design experts reached 92.5%.While the experts of teaching materials developed reached 89.3%.The results of trials on students, the overall eligibility percentage reached 83.66%.While the Review from the teacher reached 78.41% and the response through the questionnaire got a very good response.From the data and analysis that has been done, it can be concluded that the development product can be used as teaching material in Pancasila and Citizenship Education (PPKn) subjects that have very good qualifications, and develop the domains of competence, character and student literacy.
Online Signature Verification Using Multi-Distance Measures and Weighting with Gradient Boosting

To improve performance of online signature verification while maintaining lower calculation cost and higher security, this study proposes a novel single-template strategy in function-based approaches. Specifically, we adopt dynamic time warping (DTW) barycenter averaging to obtain an effective mean template while preserving intra-user variability between all references. Then, by using the mean template, we calculate multi-distance measures: The multiple DTW from each feature with independent warping and the single DTW from all features with dependent warping. To boost the discriminative power, we apply a weighting scheme using gradient boosting to efficiently combine the multi-distance measures. The promising performance is demonstrated through its application to a popular SVC2004 Task2 dataset.
Robust object tracking via online discriminative appearance modeling

Abstract A robust object tracking algorithm is proposed in this paper based on an online discriminative appearance modeling mechanism. In contrast with traditional trackers whose computations cover the whole target region and may easily be polluted by the similar background pixels, we divided the target into a number of patches and take the most discriminative one as the tracking basis. With the consideration of both the photometric and spatial information, we construct a discriminative target model on it. Then, a likelihood map can be got by comparing the target model with candidate regions, on which the mean shift procedure is employed for mode seeking. Finally, we update the target model to adapt to the appearance variation. Experimental results on a number of challenging video sequences confirm that the proposed method outperforms the related state-of-the-art trackers.
Matrix-Product Codes over Commutative Rings and Constructions Arising from<math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mfenced open="(" close=")" separators="|"><mrow><mi>σ</mi><mo>,</mo><mi>δ</mi></mrow></mfenced></math>-Codes

A well-known lower bound (over finite fields and some special finite commutative rings) on the Hamming distance of a matrix-product code (MPC) is shown to remain valid over any commutative ring <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mi>R</mi></math> . A sufficient condition is given, as well, for such a bound to be sharp. It is also shown that an MPC is free when its input codes are all free, in which case a generator matrix is given. If <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><mi>R</mi></math> is finite, a sufficient condition is provided for the dual of an MPC to be an MPC, a generator matrix for such a dual is given, and characterizations of LCD, self-dual, and self-orthogonal MPCs are presented. Finally, the results of this paper are used along with previous results of the authors to construct novel MPCs arising from <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><mfenced open="(" close=")" separators="|"><mrow><mi>σ</mi><mo>,</mo><mi>δ</mi></mrow></mfenced></math> -codes. Some properties of such constructions are also studied.
Improving OCR for Historical Documents by Modeling Image Distortion

Archives hold printed historical documents, many of which have deteriorated. It is difficult to extract text from such images without errors using optical character recognition (OCR). This problem reduces the accuracy of information retrieval. Therefore, it is necessary to improve the performance of OCR for images of deteriorated documents. One approach is to convert images of deteriorated documents to clear images, to make it easier for an OCR system to recognize text. To perform this conversion using a neural network, data is needed to train it. It is hard to prepare training data consisting of pairs of a deteriorated image and an image from which deterioration has been removed; however, it is easy to prepare training data consisting of pairs of a clear image and an image created by adding noise to it. In this study, PDFs of historical documents were collected and converted to text and JPEG images. Noise was added to the JPEG images to create a dataset in which the images had noise similar to that of the actual printed documents. U-Net, a type of neural network, was trained using this dataset. The performance of OCR for an image with noise in the test data was compared with the performance of OCR for an image generated from it by the trained U-Net. An improvement in the OCR recognition rate was confirmed.
A Study of Text Steganography Methods

Based on the development of information and communication technology, then the security improvement will be more important.One of them is the level of security in inserting data or information.Increased security to insert data and information can be resolved using steganography technique.Steganography itself is a technique to hide messages into a digital medium.The digital media used in this steganography is text, picture, sound and video.In the text media, the methods and techniques used there are various.In this study, we will discuss the methods and techniques as well as comparing of each methods.
A Comprehensive Multisensor Dataset Employing RGBD Camera, Inertial Sensor and Web Camera

Over the decades, fitness activities and extreme endurance events are expanding throughout the world. The number of available public skeletal repositories and recognition/evaluation benchmarks has grown rapidly since Microsoft manufactured a motion sensing device called Kinect. Kinect RGBD data has become a very useful representation of an indoor scene for solving activity/fitness recognition problems. The other alternative sensor which has been utilized widely in this area is the wearable inertial measurement unit (IMU) sensor. With numerous advance sensors with mass adoption, this technology represents a possible approach to surpass current activity recognition and evaluation research solutions. Nevertheless, there is a limited number of publicly available datasets where depth camera, inertial sensor, and RGB image data are captured at the same time. In this paper, we introduce NCTU-MFD (National Chiao Tung University Multisensor Fitness Dataset), a comprehensive, diverse multisensor dataset collected using Kinect RGBD sensor, wearable inertial sensors, and web cameras. The dataset contains 47131 RGB images, 47131 depth images, and 100 csv files including 47131 skeletal data (from 25 joints) collected from Kinect sensor. In addition, our dataset also contains acceleration and gyroscope data from IMU sensors, and 94262 RGB images (47131 images from each web camera). To demonstrate the possible use of our dataset, we conduct an experiment on evaluation of depth maps.
Strong geodetic problem on complete multipartite graphs

The strong geodetic problem is to find the smallest number of vertices such that by fixing one shortest path between each pair, all vertices of the graph are covered. In this paper we study the strong geodetic problem on complete bipartite graphs. Some results for complete multipartite graphs are also derived. Finally, we prove that the strong geodetic problem restricted to (general) bipartite graphs is NP-complete.
Determination of effective stress intensity factors under mixed‐mode from digital image correlation fields in presence of contact stresses and plasticity

Abstract Digital image correlation (DIC) is more and more popular to monitor fatigue crack growth and to determine the stress intensity factors. However, the posttreatment of the recorded displacement fields becomes tricky when the crack faces are not stress‐free and when crack tip plasticity becomes significant. Several posttreatment methods to locate the crack tip and measure the effective stress intensity factors in such cases are compared, using finite element method‐computed displacement fields, and then used on real DIC fields. An approach coupling DIC and finite element method is proposed to estimate the contact stresses along the crack.
Probability Distribution of Rainfall in Medan

In this paper we chose three stations in Medan City , Indonesia to estimate Monthly Rainfall Data i.e. Tuntungan, Tanjung Selamat, and Medan Selayang Stations. We took the data from 2007 to 2016. In this case fitted with Normal, Gamma, and Lognormal Distributions. To estimate parameters, we used this method. Furthermore, Kolmogorov-Smirnov and Anderson Darling tests were used the goodness-of-fit test. The Gamma and Normal Distributions is suitable for Tuntungan and Medan Selayang Stations were stated by Kolmogorov-Smirnov's test. Anderson Darling's test stated that Gamma Distribution was suitable for all stations.
GOOGLE MAPS AND MAPBOX API PERFORMANCE ANALYSIS ON ANDROID-BASED LECTURE ATTENDANCE APPLICATION

Attendance is an inseparable component from lectures. The current manual attendance process still has its weaknesses, such as the loss or broken attendance sheets, the easiness to conduct fraud on the attendance sheets, and other cases. The attendance system using fingerprint devices are also not available in other locations yet, such as lectures that are done on the field or outdoor, where fingerprint devices are not available. Because of that, an online lecture attendance system that makes use of Android-based smartphones is developed in order to tackle the problem. This online lecture attendance system has the main feature of recording students’ attendances in a radius of 300 metres from the lecturer. This application is named Lecture Attendance System is developed using Rapid Application Development (RAD) model, because it is an effective method to minimise errors in the application. There are two testing performed to the application. The first testing was functional testing of the application. This testing was carried out in order to make sure that all functionalities and features are performing well. The second testing carried out is the distance accuracy testing, to compare between the Google Maps and MapBox API distances. From this testing, it was found that the error percentage using Google Maps is 9.250% and 12.128% for MapBox. From these results, they show that in calculating the distance, using Google Maps API has higher accuracy compared to the MapBox API.
Medical video compression using bandelet based on lifting scheme and SPIHT coding: In search of high visual quality

This paper investigates a coding method for medical video compression. The described technique has advantages of providing a higher visual quality of video coding, and efficiently reducing spatial redundancy. Firstly the bandelet transform, also called a non-separable transform, is studied in order to allow an efficient detection of the different complex geometries found in video. After describing the lifting operation in the bandelet transform, we reduced the artifactual effect caused by the quad-tree decomposition step in order to enhance the visual quality of the medical video sequence. Finally, the efficiency of the proposed method on medical video is tested and evaluated by means of a set of objective measurement parameters.
Conformal Redirected Walking for Shared Indoor Spaces

In this work we present a redirected walking scheme suitable for shared spaces in a virtual reality environment. We show our redirected walking to work for the case of two physical spaces (a host and a guest) being merged into a single virtual host space. The redirection is based on warping the guest space into the host space using a conformal mapping that preserves the local shape and features. We compare our technique with state-of-the art indoor redirection schemes and show its efficiency. We found our method to have better task performance, higher social presence, and less simulator sickness.
Algebraic Properties of the Block Cipher DESL

The Data Encryption Standard Lightweight extension (DESL) is a lightweight block cipher which is very similar to DES, but unlike DES uses only a single S-box. This work demonstrates that this block cipher satisfies comparable algebraic properties to DES—namely, the round functions of DESL generate the alternating group and both ciphers resist multiple right-hand sides attacks.
A Career Track Recommender System for Senior High School Students using Fuzzy Logic

Rule-based algorithms are mostly used in recommender systems but still, cannot address the issue of uncertainty in decision making particularly to senior high school students in choosing the right career track because of numerous influential factors that may affect their decisions.That is why a career track recommender system using fuzzy logic has been developed to address this issue.In this paper, the significant factors that is most influential to the decision of the students as best attributes were determined using feature selection filtering techniques and used as crisp inputs.The result shows that the developed fuzzy model performs a high predictive accuracy based on the computed mean absolute error (MAE) and root-mean-square error (RMSE) scores which decreases from the training, to the validation and test sets.The recommendation returns the best possible result based on the computed normalized discounted cumulative gain (nDCG) which is 0.948 from the desired to the actual preference of students which is almost near to 1.0.With these, the developed recommender system is highly recommended as perceived by the users in terms of usability, maintainability and portability.
Security-enhanced optical encryption based on JTC architecture with confused ciphertext

Security is the key issue of an optical cryptosystem. In this work, a security-enhanced optical encryption scheme based on JTC architecture with confused ciphertext is demonstrated. Firstly, the original image is decomposed into two phase-only masks. Then one of the masks is transformed into joint power spectrum (JPS) by using a JTC architecture. Finally, the JPS is confused by operations of rotation and superposition, and the obtained data is employed as the final ciphertext. As a consequence, the proposed scheme removes the linear character of the conventional JTC-based cryptosystem. Therefore, the proposed scheme achieve a high robustness against attacks based on linearity of the cryptosystem, especially the attack based on iterative Fourier transform (IFT). Furthermore, good performances of simplicity and low cost are remained at the same time. Some numerical simulations are presented to verify the validity and performance of the security-enhanced scheme.
Comparing MT Approaches for Text Normalization

One of the main characteristics of social media data is the use of non-standard language.Since NLP tools have been trained on traditional text material, their performance drops when applied to social media data.One way to overcome this is to first perform text normalization.In this work, we apply text normalization to noisy English and Dutch text coming from different genres: text messages, message board posts and tweets.We consider the normalization task as a Machine Translation problem and test the two leading paradigms: statistical and neural machine translation.For SMT we explore the added value of varying background corpora for training the language model.For NMT we have a look at data augmentation since the parallel datasets we are working with are limited in size.Our results reveal that when relying on SMT to perform the normalization, it is beneficial to use a background corpus that is close to the genre to be normalized.Regarding NMT, we find that the translations -or normalizations -coming out of this model are far from perfect and that for a lowresource language like Dutch adding additional training data works better than artificially augmenting the data.
Multistage Analysis of Abnormal Human Behavior in Complex Scenes

Effective abnormal human behavior analysis serves as a warning signal before emergencies. However, most abnormal human behavior detections rely on manual monitoring at present. This method is criticized for being subjective and lack of timeliness. In response to the problems above, this paper proposes a multistage analysis method of abnormal human behavior in complex scenes. This paper firstly differentiates the abnormal behavior roughly from a large monitoring area with similarity measurement applied to the social force model, and precise analysis is conducted thereafter. The multistage analysis, based on the three-frame difference algorithm, is used for intrusion, left-behind baggage detection, and motion trajectory identification. The experimental result demonstrates the superiority of the proposed method in UMV, CAVIAR, and datasets. To demonstrate the adaptability and generalization ability of the proposed method, this paper selects the CVC and JAAD driving anomaly detection data sets to test the method. Experimental results show that the proposed method is superior to the existing methods.
Number systems in modular rings and their applications to "error-free" computations

The article introduces and explores new systems of parallel machine arithmetic associated with the representation of data in the redundant number system with the basis, the formative sequences of degrees of roots of the characteristic polynomial of the second order recurrence. Such number systems are modular reductions of generalizations of Bergman's number system with the base equal to the "Golden ratio". The associated Residue Number Systems is described. In particular, a new "error-free" algorithm for calculating discrete cyclic convolution is proposed as an application to the problems of digital signal processing. The algorithm is based on the application of a new class of discrete orthogonal transformations, for which there are effective “multipication-free” implementations.
Predicting Algorithm Classes for Programming Word Problems

We introduce the task of algorithm class prediction for programming word problems.A programming word problem is a problem written in natural language, which can be solved using an algorithm or a program.We define classes of various programming word problems which correspond to the class of algorithms required to solve the problem.We present four new datasets for this task, two multiclass datasets with 550 and 1159 problems each and two multilabel datasets having 3737 and 3960 problems each.We pose the problem as a text classification problem and train neural network and non-neural network based models on this task.Our best performing classifier gets an accuracy of 62.7 percent for the multiclass case on the five class classification dataset, Codeforces Multiclass-5 (CFMC5).We also do some human-level analysis and compare human performance with that of our text classification models.Our best classifier has an accuracy only 9 percent lower than that of a human on this task.To the best of our knowledge, these are the first reported results on such a task.We make our code and datasets publicly available.