Reviewerly/exHarmony huggingface dataset:
Retail Customer Acceptance Toward QR Code Payment System in Malaysia

The purpose of this study is to analyze the significant factors that affect the acceptance of retail customers toward Quick Response (QR) code payment system in Malaysia. Cross sectional study was conducted by using convenient sampling which data collected through online survey. The result showed there was significant positive fair correlation between ease of use and acceptance of QR code payment (p value < 0.001, r = 0.796), significant positive fair correlation between social influence and acceptance of QR code payment (p value < 0.001, r = 0.686) and significant positive fair correlation between security and acceptance of QR code payment (p value < 0.001, r = 0.753). The findings provide useful insight into how the retailers should implement QR code payment system efficiently as it will enhance their customer service as most of the retail customers’ opted QR code payment system due to its convenience in terms of ease of use.
Survey Software Implementations of Homomorphic Encryption Methods

Homomorphic Encryption (HE) is a perspective form of encryption that allows computing on encrypted data. Potential applications include combining sensitive encrypted data in a cloud environment and performing calculations on it in the cloud without compromising data privacy. Due to the active interest in this area, many new schemes of Fully Homomorphic Encryption are being developed. We implement two optimized versions of HE: the Brakersky-Fan-Vercauteren (BFV) and Cheon-Kim-Kim-Song (CKKS) schemes, and evaluate their performance. The most interesting (and also unexpected result of our performance assessment) is that the CKKS variant in practice scales significantly better (usually several times) with an increase in the multiplicative depth of the computational schemes than the BFV, implying that the CKKS variant will always surpass the BFV for most practical applications. The unexpected result was that the decoding and KeyGen functions for 128-bit security settings show the superiority of the BFV scheme, however, if we consider the whole process of encryption, decryption and operation above ciphertexts, CKKS performance exceeds BFV by more than 100 times, which gives a reliable result of our research.
Multiprocessing Implementation for Building a DNA q-gram Index Hash Table

Over the past few years, next-generation sequencing has become an invaluable technology for numerous applications in the field of genomics. The success of these applications are dependent on the performance of each phase in the genomic sequence pipeline, which starts with read mapping. However, read mapping is computationally intensive since it requires mapping billions of reads to numerous locations in a large reference genome. Building a q-gram index hash table has proven to be an efficient alternative to reduce the repetitive scanning of the reference during the verification step. A q-gram index hash table stores the locations of each q-gram in the reference genome. To accelerate the process of building this data structure and to exploit the multi-core architecture, instructions can be executed in parallel and distributed to multiple CPU cores. This paper performs a comparison analysis between the sequential and multiprocessing implementation of the index build time of the three methods for building a q-gram index hash table. The implementation results show that all multiprocessing versions are faster than sequential ones, with speedups ranging from 1.53 to 2.57. Although the open addressing method yields the fastest index build time, the best speedup is achieved by the minimizer-based method.
Improvement of digital phase shifting profilometry skin imaging by combination of frequency and spatial filtering

Phase shifting profilometry (PSP) has been widely used in many fields of application due to its accuracy and precision in obtaining depth information of a surface. One of the many applications that can take advantage of phase shifting profilometry is skin imaging, where PSP can be beneficial in efficacy testing of a certain cosmetic product, scar healing monitoring, etc. The simplicity of digital phase shifting profilometry hardware configuration further support the use of this technology for skin imaging application. Even though PSP and other interferogram based measurement is well known of its insensitivity to background lighting—because of its utilization of structured light pattern as light modulator—in practice, light interaction with surface finish still contributes to error to some extent. In this paper, the combination of frequency filter and spatial filtering is used to compensate that problem as preprocessing step prior to phase analysis. A simple Butterworth bandpass filter is used to isolate the first order harmonic containing modulation information, followed by Mean Curvature Diffusion to eliminate illuminance noise due to reflection from skin surface. Validation of this method was carried out by testing samples of leather having different textures.
Copy–Move Forgery Detection by Using Key-Point-Based Harris Features and CLA Clustering

Images can easily be manipulated without any visual marks to the naked human eye with massive improvements in image manipulation software. This tampering is the main propelling force for the need of better image forensics such that field is known as image forgery detection. Any digital image with regions where the image contents are identical is said to have copy–move forgery (CMF). Copy–move forgery is performed to improve the visual features or to cover the underlying truth in the image. Many algorithms have been used for CMF detection, and this work is about improved key-point and clustering-based CMF detection scheme. The proposed scheme combines the efficiency of a key-point-based scheme and clustering of these key points to further improve the results. Modified Harris operator-based key-point detection algorithm with clustering using local gravitation is utilized for key-points selection. The average accuracy, PSNR and SSIM rates are used to evaluate the performance of the proposed algorithm with scale-invariant feature transform (SIFT), which is another state-of-the-art key-point algorithm. The paper concluded with the efficiency of the key-point-based scheme.
Toward Unseating the Unsafe C Programming Language

Reflecting on content that I taught in a recent security course about software-based vulnerabilities, I wondered: Am I giving too much focus to the C programming language? C-based examples get straight to the point, allowing compact illustrations of the concepts underlying stack-and heap-based buffer overruns and return-oriented programming, aside from integer-based vulnerabilities, related to arithmetic underflow, conversions between signed and unsigned values, and errors due to the compiler promotion of short-integer data types in arithmetic expressions.1 But are these relevant for today's students, given the wide availability of modern languages with strong language safety properties? Unfortunately, the answer is still yes.
Embedding an Integrated Security Management System into Industry 4.0 Enterprise Management: Cybernetic Approach

The paper contains the results of the analysis of methodologies and standards obtaining the requirements to security management systems of enterprises including modern enterprises implementing Industry 4.0 principles. Key standards ISO/IEC 7498, 15408, 18045, 20000, 27000 have been analyzed to suggest an approach to the development of integrated security and safety management system structure considering threats of intrusion into physical, information and signal spaces. This system is part of the enterprise management system and based on cybernetic principle of control. These subsystems check and control according with individual and general objectives for physical, information and signal spaces and requirements-based models. The goal of the paper is to analyze the methodologies for developing an integrated security management system structure as a component of enterprise management systems in the context of Industry 4.0. The obtained results and recommendations for enhancing and implementation these systems are discussed.
Study of Experimental Teaching Mode Based on OBE

Outcome based education (OBE) is an advanced educational concept. To improve the quality of experimental teaching, Ground Mobility Experimental teaching Center of Beijing Institute of Technology relies on the experimental teaching reform project, carries out the research into the experimental teaching mode around the OBE results, preparation of experimental teaching, experimental teaching implementation, evaluation of experimental teaching quality, and continuously improves the basic concepts. It improves the experimental teaching quality effectively, as well as the students' ability; it guides and promotes the teaching reform, guarantees and improves the training quality of the talents in engineering.
Native Resolution Detection for 4K-UHD Videos

There are many resolution enhancement methods to produce 4K-UHD videos including interpolation and deep learning based Super-Resolution, but lack of details, aliasing and unnatural transition are still inevitable and affect viewer's immersive experience. Therefore, it is necessary to detect authenticity and native resolution of 4K-UHD videos. In this paper, we propose a new native resolution detection method for 4K-UHD videos. The method is based on extracting feature from the DCT frequency domain and integrating this feature in all frames. Our method can determine authenticity of 4K-UHD videos as well as native resolution, no matter which kind of SR model is applied for upscaling.
Perception detection using Twitter

Patients' perceptions about their condition have a strong impact on not only adherence to medication, but also on how they view themselves in the light of their condition. Research implies that Twitter is a particularly rich source of perceptions, as patients frequently use internet for information sharing and support. However, Twitter contains a lot of noise in the form of tweets that do not relate to perceptions, but are rather generated to advertise research and corporate news and this kind of information could `pollute' perception analysis. This study examined methods that could be used to extract perception tweets, on the example of tweets related to asthma. We first demonstrated differences between perception and non-perception tweets in terms of their linguistic features, and then focused on filtering perceptions using the classification process. Results demonstrated that there is a significant difference between perceptions and non-perceptions: perception tweets are shorter, have less capital letters, less punctuation signs and less hashtags. These features also performed well in predicting perceptions. However, the bag of words approach had better results in distinguishing between perception and non-perception tweets and the best results were obtained using word-based frequency vectorization and by training a neural network based classifier. Future research could explore the synergy of these approaches.
Coupler characterization of superconducting transmons qubits for cross-resonance gate

We characterize two-qubit cross-resonance gates and unintended residual coupling on various coupled qubit arrangements. Direct coupling versus coupling via a quantum bus are studied on the basis of cross-resonance gate rate and fall-off of non-nearest neighbor coupling. We experimentally extract coupling rates using Hamiltonian tomography methods, and compare with microwave simulations.
Ethical hacking and penetration testing for securing us form Hackers

Abstract Almost all the people around the world are fully equipped with technologies and software’s. But the how many are really using it wisely, we as the creators of all technologies are being governed by such a technology itself. The software’s are being discovered every day and each one provides a new facility and a hidden disadvantage in it. Many people don’t even have the basic knowledge of what they are doing and that’s the main reason for all cyber-crimes and security breaches. It has become responsibility of every citizen who is using a tech to keep their information safe and secure from others. Be aware of what we are doing in mobiles, laptops, PCs, etc.… since a small activity could lead to great crisis.
The web-based estimation of motorcycles sales using linear regression method

Abstract The purpose of this research is to use the linear regression method to predict motorcycle sales results, the variable used is the period as an independent variable (X) and sales as the dependent variable (Y). The data used in the calculation of linear regression is the last four years data, from January 2014 to December 2019. The implementation of the motorcycle sales forecasting system is to predict sales in the coming months. To find out the level of accuracy of the prediction error calculation is needed so that it is known how many error levels are obtained. Calculation of forecasting errors using Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). The results of this study are web based motorcycle sales prediction systems using linear regression method. From this system, motorcycle sales forecasting is obtained the following month. In January 2015 with forecasting results of 12.63. To find out how accurate the forecasting level is, the error calculation result using Mean Absolute Deviation (MAD) is 3.40 and Mean Absolute Percentage Error (MAPE) is 44.33%. The results show that the error rate is small and the forecasting results are close to accurate.
Investigation of Node Pruning Criteria for Neural Networks Model Compression with Non-Linear Function and Non-Uniform Network Topology

This paper investigates node-pruning-based compression for non-uniform deep learning models such as acoustic models in automatic speech recognition (ASR). Node pruning for small footprint ASR has been well studied, but most studies assumed a sigmoid as an activation function and uniform or simple fully-connected neural networks without bypass connections. We propose a node pruning method that can be applied to non-sigmoid functions such as ReLU and that can deal with network topology related issues such as bypass connections. To deal with non-sigmoid functions, we extend a node entropy technique to estimate node activities. To cope with non-uniform network topology, we propose three criteria; inter-layer pairing, no bypass connection pruning, and layer-based pruning rate configuration. The proposed method as a combination of these four techniques and criteria was applied to compress a Kaldi's acoustic model with ReLU as a non-linear function, time delay neural networks (TDNN) and bypass connections inspired by residual networks. Experimental results showed that the proposed method achieved a 31% speed increase while maintaining the ASR accuracy to be comparable by taking network topology into consideration.
SLKOF: Subsampled Lucas-Kanade Optical Flow for Opto Kinetic Nystagmus detection

The neurological disorders are developed in adults due to reduced visual perception. Opto Kinetic Nystagmus (OKN) is a clinical method to detect visual perception. For objective measurements, a computational algorithm based OKN detection is preferable rather than clinical practice. In this paper, a memory-efficient Subsampled Lucas-Kanade Optical Flow (SLKOF) is proposed. The proposal employs the Subsampling of images for various levels. The proposal deals with the computation of OKN gain for different image Subsampling factors using the MATLAB platform. The experimental set up to observe OKN is done using computer-based rotation control of the drum through a stepper motor. The results are compared with the well established Lucas-Kanade (LK) method for Optical flow. It is observed that OKN gain corresponds to 1/4th of a subsampled image of the SLKOF method correlates with the LK method for the majority of the cases. This validation evidently elucidates that the proposal is computationally efficient.
Online Child Pornography: Problem Overview and Global Trends in Counteraction

The authors discuss the online dissemination of child pornography. The authors analyze this phenomenon in the contemporary world and state that such crimes are large-scale and occur on all territories. At the same time, the authors claim that modern criminology does not have any special research methods of monito­ring cyber-crimes in general, or online child pornography specifically. They note that research cannot keep up with the rapid pace of information and telecommunication technologies' progress and that criminological research in this sphere is lagging behind. Applying the comparative method, the authors studied the criminalization of the online dissemination of child pornography in national legislations of different countries. It is stated that national legislations contain various tasks of counteracting the spread of online child pornography, ranging from the criminalization of this phenomenon to the improvement of the technical means of countering it. It is suggested that such a divergence of tasks is connected with the social, economic and technological development of different countries. The use of systemic analysis made it possible to specify key trends in counteracting the analyzed phenomenon. One of them is the replacement of the term «child pornography» in normative documents by the term «sexual violence against children». Violence is a constructive feature of child pornography. The authors explain he grounds for such a replacement. They describe the methods of counteracting the spread of online child pornography used in diffe­rent countries, and suggest conditionally dividing them into normative, organizational and technical ones. It is noted that NGOs and international organizations play a significant role in countering the dissemination of online child pornography. The authors outline the most relevant prospects of international cooperation in countering the sexual exploitation of children, the key of which is more active interaction between states, inclu­ding interaction under the auspices of international organizations.
EEG authentication system using fuzzy vault scheme

Authentication has become a necessity these days because the use in most of the life applications from accounts to devices, especially with the rapid development of technology versus traditional methods such as password and PIN, that is, become infeasible for such methods. New methods have emerged, including biometric systems. In this paper we will explore the possibility of combining cryptography with biometrics in order to achieve authentication. EEG will be used as they are unique and also difficult to expose and copy of 9 healthy persons, extracting features from them and use that features with fuzzy vault scheme to provide high security to encrypt Biometric systems. the calsssification gives good accuracy 96% , and using the tent chaff points give the system an advantage because it reduces the error occurs when separates chaff points from the genuine point which are the EEG signal features because the initial seeds are known by both sender and receiver.
A knowledge graph of medical institutions in Korea

Medical institutions in Korea are classified according to various legal standards. Health insurance is divided into primary and secondary medical institutions; but, the medical delivery system is classified into tertiary institutions such as clinics, local hospitals, and general hospitals. Thus a unified classification constructed by interlinking the related categories is required. In this study, a knowledge model for representing medical institutions and their characteristics based on related laws is proposed, and a knowledge graph that includes all medical institutions in Korea is constructed. In particular, this graph can be interlinked to heterogeneous data sources such as administrative districts, thereby enabling users to identify appropriate hospitals or other institutions according to their requirements.
Compilation of Function Representations for Secure Computing Paradigms

This paper introduces M-Circuits, a program representation which generalizes arithmetic and binary circuits. This new representation is motivated by the way modern multi-party computation (MPC) systems based on linear secret sharing schemes actually operate. We then show how this representation also allows one to construct zero knowledge proof (ZKP) systems based on the MPC-in-the-head paradigm. The use of the M-Circuit program abstraction then allows for a number of program-specific optimizations to be applied generically. It also allows to separate complexity and security optimizations for program compilation from those for application protocols (MPC or ZKP).
Deep Learning Multimodal for Unstructured and Semi-Structured Textual Documents Classification

Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information, the document classification task becomes an interesting area for controlling data behavior. This paper presents a document classification multimodal for categorizing textual semi-structured and unstructured documents. The multimodal implements several individual deep learning models such as Deep Neural Networks (DNN), Recurrent Convolutional Neural Networks (RCNN) and Bidirectional-LSTM (Bi-LSTM). The Stacked Ensemble based meta-model technique is used to combine the results of the individual classifiers to produce better results, compared to those reached by any of the above mentioned models individually. A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques. These techniques include using Term Frequency Inverse Term Frequency (TFIDF) or Continuous Bag of Word (CBOW) to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models. Moreover, this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class. In addition, the experimental results prove that the proposed model has achieved effective performance. Besides, upon investigating the PDF Documents classification, the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features, respectively. Moreover, concerning the JSON Documents classification, the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features, respectively. Furthermore, as for the XML Documents classification, the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features, respectively.
Customized Adaptive Gradient and Orientation histogram for faces altered by Face surgery

Recently, the ability of different algorithms to create complexity of face recognition is exchanged with. plastic surgery due to skin differences. Although plastic surgery is a serious problem in facial expression, a major concern to be studied in terms of hypothetical and diagnostic ideas. In this article, it is anticipated that the removal of the histogram-based markers of the site of formation and adaptive gradient adaptation (AGLOH) will allow obtaining an effective facial recognition of plastic surgery. The elements are extracted from a small gap of the granules of the surface area. In the textbook, a variety of local binary models will outline moving AGLOH characteristics. Therefore, magnitude of characteristics is abbreviated as primary object analysis as it provide training to ANN. In essence, neural network is made up of trainees who use particle swarm optimization (PSO) rather than standard learning algorithms. These tests are performed on 150 face-to-face plastic surgeries, including blepharoplasty, eyebrow lifting, lip shaving, malar enlargement, mentoplasty, otoplasty, rhinoplasty, rhytidectomy, skin peeling. Finally, it is proved that the algorithm proposed in the article shows its unique performance.