Reviewerly/exHarmony huggingface dataset:
Quantum neural networks with multi-qubit potentials

Abstract We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multi-qubit potentials in the quantum perceptrons enables more efficient information processing tasks such as XOR gate implementation and prime numbers search, while it also provides a depth reduction to construct distinct entangling quantum gates like CNOT, Toffoli, and Fredkin. This simplification in the network architecture paves the way to address the connectivity challenge to scale up a quantum neural network while facilitating its training.
Local Migration Model of Images Based on Deep Learning against Adversarial Attacks

Deep Neural Networks (DNNs) have achieved remarkable results in various tasks. However, DNNs are easily deceived by small input disturbances, which are called adversarial attacks. The adversarial attack is to deliberately add some subtle interference that humans cannot detect to the input sample, causing the model to give a wrong output with high confidence. Deep-Learning-as-a-Service (DLaaS) has become a current hot trend, and it also introduces challenging security issues. Therefore, in this paper, we propose a local migration model of confrontational attack images based on deep learning. The confrontational examples of the physical world are disguised as natural styles through the migration model to deceive human observers. Specifically, the model converts the small counter-interference into a specific pattern, and then camouflages the foreground or background or local target area of the image to achieve a high degree of invisibility. Due to the flexibility of the interference setting of this method, it can be used to help DNNs assess their robustness, and it can be used to achieve privacy protection and data security detection.
Design of Basic Computer Neworking Simulation Learning using Multimedia Development Life Cycle Method based on Augmented Reality at SMKN 1 Tangerang

Augmented Reality has entered technology trends in all fields, both marketing and education, Augmented Reality media also requires users to be comfortable when interacting. SMKN 1 Tangerang organizes a distance education system with various methods, in this case the subject which is Basic Computer Networks uses mobile-based learning simulations with Augmented Reality which aims to develop students' abilities in the world of networked computers independently. This system also requires a user experience and user interface development method, namely Luther's Multimedia Development Life Cycle model because it has six stages, namely concept, design, data collection, merging, testing, and distribution, resulting in interactive applications.
Sentiment Classification Performance Analysis Based on Glove Word Embedding

Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pretrained, embedded and deterministic word embedding classification performance has analyzed by using Long Short Term Memory (LSTM). The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.
Reading and its relationships among attitude, motivation, and comprehension achievement in upper secondary school students

Reading is essential in every subject. Reading also has a relationship with the students' achievement. This study is aimed to investigate whether or not there is (1) a significant correlation between reading attitude and reading comprehension achievement, (2) a significant correlation between reading motivation and reading comprehension achievement, (3) a significant correlation between reading attitude and reading motivation, and (4) any correlation between predictor variables and the criterion variable. The study was a correlational study with Pearson-product moment correlation. The sample of this study were 95 eleventh graders of upper secondary school. Cluster random sampling technique is used in this study. Questionnaires of reading attitude, reading motivation, and reading comprehension tests are given. The questionnaire result reveals that the reading attitude was negative, and reading motivation is medium. Furthermore, the results show that reading attitude and reading comprehension has a significant correlation. Reading motivation and reading comprehension also has a significant correlation. The roles of students, parents and teachers must work suitably in order to construct the positive reading atmosphere, thus they could achieve the better achievement in reading even for all subjects.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; The supports from family in motivating the children to be a good reader could build the positive attitude toward reading.&#x0D; Reading motivation can lead to the improvement in language learning performance.&#x0D; The learners who tend to read simple English texts can be said as the learners who were lacking English reading experiences.&#x0D;
Some Workout Problems on Motion of Raindrop

The motion of rain drop through atmosphere is an interesting classical problem because of the fact that air resistance and moisture accretion are integral part of it. Mathematical modeling of it using Newtonian formalism is considered here and discussions are made for no mass accretion and air resistance proportional to nth power of velocity. We use python program and library extensively to find the terminal velocity of rain drop. Graphs show close agreement and velocity power up to n=3 is good.
Correction to: A new approach to q-linear Diophantine fuzzy emergency decision support system for COVID19

In the original article, the affiliation of first author has been incorrectly published.
Artificial intelligence (AI) in recruiting talents Recruiters’ intention and actual use of AI

Artificial intelligence (AI) is a fast-growing field of study in the computer science domain that emulates specific human characteristics to catch up with the rapid advances in human life. Social influence refers to the magnitude to which users accept that essential others in their society believe and expect users should use the new technology. Studies opined that social norms and images and important others are essential during deciding as to adopt the technology. The adoption of technology requires proper organizational and technical supports to get expected utility from using a novel technology. The present study investigated the adoption and the actual use of AI in recruiting talents from a developing country’s perspective, specifically in Bangladesh. Hypotheses and research questions were developed from the ideation of Unified Theory of Acceptance and Use of Technology. Adoption and implementation of AI for recruiting talent by the human resource professionals might result in efficient and effective recruitment promptly.
Acceptance and Assessment in Student Pair-Programming: A Case Study

This study analyzes pair programming's acceptance and assessment in the university setting considering participants' gender, previous programming ex-perience, and programming enjoyment. The sample included 80 students from three different sections enrolled in a basic programming course. We used a questionnaire to collect data after the pair programming practices. For data analysis, we used SPSS 24, and Mann-Whitney, Kruskal-Wallis, and Jonckheere-Terpstra statistical techniques. Descriptive and comparative re-sults showed a significant increasing monotonic trend in the acceptance of pair programming as students' preference toward programming increased (standardized statistic = 3.20, p = 0.00, Kendall's τb = 0.30, p = 0.001). Also, pair programming was positively accepted and assessed even by students who reported a low level of programming enjoyment. There were no other statistically significant results.
Analysis and Implementation of Honeyd as a Low-Interaction Honeypot in Enhancing Security Systems

Every computer connected to a wide computer network is vulnerable to the occurrence of data, information, resources and services that exist in the system from actions such as intrusion, wiretapping, theft and misuse of important data to damage to network systems, which are carried out by irresponsible intruders, wiretapping, theft and misuse of important data by individuals, groups, within a company/government agency or private sector, even damage to computer network systems may occur. in a company, which is done by an intruder or attacker who is not responsible. Honeypot honeyd is a method that can be applied, implemented in medium to large scale companies, especially those that have implemented computer-based systems and technology, to prevent, anticipate bad actions before they occur and take quick action when bad impacts occur. Honeypot honeyd with low-interaction, which is to interact indirectly with the attacker, because honeyd positions itself as a bait or a shadow server that is deliberately attacked so that the results of the attack can be known and analyzed. In this research, honeyd honeypot is a shadow server that resembles a real server, which has several services along with ports that are deliberately opened for attack. The results of this research can be seen that there is an infiltration or direct attack, seen from the increase in network traffic above normal on the monitor system, and also can be seen log files from Honeyd in detail what the attackers have done or are currently doing to be analyzed and then take precautions, anticipation, socialization of security in carrying out activities that are directly related to the outside world through the network, improving both servers, network systems and existing services. Thus the honeyd honeypot can help save important data, resources and can improve computer network security systems.
Methods for Integrating Extraterrestrial Radiation into Neural Network Models for Day-Ahead PV Generation Forecasting

Variability, intermittency, and limited controllability are inherent characteristics of photovoltaic (PV) generation that result in inaccurate solutions to scheduling problems and the instability of the power grid. As the penetration level of PV generation increases, it becomes more important to mitigate these problems by improving forecasting accuracy. One of the alternatives to improving forecasting performance is to include a seasonal component. Thus, this study proposes using information on extraterrestrial radiation (ETR), which is the solar radiation outside of the atmosphere, in neural network models for day-ahead PV generation forecasting. Specifically, five methods for integrating the ETR into the neural network models are presented: (1) division preprocessing, (2) multiplication preprocessing, (3) replacement of existing input, (4) inclusion as additional input, and (5) inclusion as an intermediate target. The methods were tested using two datasets in Australia using four neural network models: Multilayer perceptron and three recurrent neural network(RNN)-based models including vanilla RNN, long short-term memory, and gated recurrent unit. It was found that, among the integration methods, including the ETR as the intermediate target improved the mean squared error by 4.1% on average, and by 12.28% at most in RNN-based models. These results verify that the integration of ETR into the PV forecasting models based on neural networks can improve the forecasting performance.
Correction to: Design of an intelligent video surveillance system for crime prevention: applying deep learning technology

In the original publication, copyright holder name was incorrect. The author regrets this mistake. The original article has been corrected.
Active contour model with local prefitting bias estimation for fast image segmentation

Intensity inhomogeneity, which is also called bias field, is ubiquitous in digital images. The causes of intensity inhomogeneity are complex and include uneven illumination and defects of imaging equipment. For images with local intensity inhomogeneity, an array of existing segmentation algorithms has poor performance on efficiency, accuracy, or initial robustness. To tackle this problem, an active contour model based on local prefitting bias estimation is proposed. The bias field is approximated through a new function based on a mean filtering algorithm, which can credibly represent the distribution of bias field of an input image. Then, the bias field is incorporated into the optimized energy functional based on the level set method to implement the segmentation process. Specifically, the bias field is computed before iterations and the mean filtering algorithm is much faster than traditional clustering algorithm, so the efficiency is greatly raised. Moreover, a new regularization function is formulated to improve the robustness of the initial contour and noise. Comparing with some traditional models, the proposed model achieves better results on some challenging images.
Global-locally reinforced feature extraction for person re-identification

Recently, the attention mechanism and part-based architectures have greatly boosted the research of person re-identification (Re-ID). However, most attention-based works extract first-order information and lack diversity. Meanwhile, the classic part-based works are not able to make use of cross-part information because of the unified partitions. These two kinds of methods ignore visual relationships in the global scope. Accordingly, we propose multi-subspace non-local attention (MSNA) and reinforced loss (R-Loss) to alleviate the issues above. MSNA is an improved attention module. It can be integrated into existing networks to utilize rich low-level information and extract the global relationships from different subspaces. R-Loss module is motivated to reinforce the capability of extracting fine-grained features by making full use of intra-part and cross-part information. We combine them and provide a global-locally reinforced feature extraction strategy. In addition, we design a feature fusion module to combine features from different branches. Equipped with the modules above, our model can extract important local and fine-grained features by identifying diverse visual relationships in the global scope. The models with our proposed modules achieve significant improvements over the baselines on four public datasets and establish new state-of-the-art results.
PROBLEM OF QUALITY OF EDUCATIONAL PROGRAMS: AUTOMATED ASSESSMENT METHOD

The article substantiates a relevant problem of education in Russia: an objective assessment of the quality of educational programs. The article substantiates the need to develop a quality system of educational programs for educational organizations in Russia. To do this, the analysis of the main normative documents regulating the educational process is carried out. These documents primarily include the Federal State Educational Standard and the professional standard. The analysis of the content of educational programs used in the activities of educational organizations is carried out. On its basis, the significance of its elements and their connection with each other is determined. An assessment package is developed to assess the compliance of each element. This allows evaluating the content in the appropriate educational program. The composition of such an assessment complex is formed based on a survey of experts specializing in the organization, control or audit of the educational process. Using this expert survey, the significance of each element is determined at the level of its corresponding criteria and the educational program as a whole. The mechanism for determining the weight values of each element for a separate level of the educational program model is presented in detail. To calculate the significance of each element, the Kendall concordance coefficient is determined and the result is evaluated using Pearson's chi-squared test with a set level of accuracy. It is found that the obtained values meet the requirements for use in the methodology for determining the quality of the educational program. The article presents the obtained values for each level of the hierarchical model of the educational program.
Facies Analysis and Depositional Environment of the Sarvak Formation (Cenomanian) in the Zagros Basin (SW of Iran)

In this study, two stratigraphic sections of Sarvak Formation were selected which are nominated Bavan and Kherameh sections. Over 379.5 meters of the Sarvak Formation and 260 thin sections have been studied. Index microfacies and foraminifers have been identified. Index microfacies include Boundstone, Wackestone and Packstone. The frequency of bioclast has been determined as larger that of the other microfacies elements. Based on the type and abundance of skeletal and nonskeletal grains and type of matrix, 14 sublithofacies have distinguished in 3 depositional environments which are include 1) Lagoon 2) Reef 3) Open marine. The mentioned determined microfacies were deposited in a Carbonate Platform of Homoclinal Ramp. Based on micropalentological data, the age of the Sarvak Formation is assigned to Cenomanian.
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and -- in asynchronous implementations -- on the gradient staleness. Especially, it has been observed that the speedup saturates beyond a certain batch size and/or when the delays grow too large. We identify a data-dependent parameter that explains the speedup saturation in both these settings. Our comprehensive theoretical analysis, for strongly convex, convex and non-convex settings, unifies and generalized prior work directions that often focused on only one of these two aspects. In particular, our approach allows us to derive improved speedup results under frequently considered sparsity assumptions. Our insights give rise to theoretically based guidelines on how the learning rates can be adjusted in practice. We show that our results are tight and illustrate key findings in numerical experiments.
Outlier detection and trimmed-average estimation in network systems

This paper addresses the problem of outlier detection and trimmed-average state estimation in an LTI network system. We consider that only some states are measured and there exists an outlier among the unmeasured states, which is so different from the remaining states that it affects the average value significantly. The goal of this paper is both to detect the outlier and to estimate the average state excluding the outlier (trimmed-average). Moreover, we also investigate the case where the system matrices are partially unknown since the outlier results from an unknown localized fault in the system. Finally, we illustrate the method on a thermal diffusion system.
ABOME: A Multi-platform Data Repository of Artificially Boosted Online Media Entities

The rise of online media has incentivized users to adopt various unethical and artificial ways of gaining social growth to boost their credibility within a short time period. In this paper, we introduce ABOME, a novel multi-platform data repository consisting of artificially boosted online media entities (also known as blackmarket-driven collusive entities), which are prevalent but often unnoticed in online media. ABOME allows quick querying of collusive entities across platforms. These include details of collusive entities involved in blackmarket services to gain artificially boosted appraisals in the form of likes, retweets, views, comments, follows and subscriptions. ABOME contains data related to tweets and users on Twitter, YouTube videos and YouTube channels. We believe that ABOME is a unique data repository which can be used as a benchmark to identify and analyze blackmarket-driven fraudulent activities in online media. We also develop SearchBM, an API and a web portal to identify blackmarket entities.
Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.
A Decentralized Copyright Protection, Transaction and Content Distribution System Based on Blockchain 3.0

The current copyright protection and copyright transactions have problems such as high platform fees, opaque transaction information, difficulty in copyright protection, and slow speed. The current interplanetary file system has gaps in file-sharing; Most blockchain frameworks do not use national secret algorithms, which causes security uncertainty. This system uses smart contract accounts to define copyrights, uses multi-person accounts to define cooperation, treats copyright deposits from the perspective of transactions, and uses many fully automatic key "center" to be responsible for the key distribution of encrypted files, which can reduce the single point pressure of the copyright center server, reduce the negative impact of single-point invalidation of the copyright center on transactions and reduce the review time. We also achieved decentralized copyright transactions and secret key transmission. By using national secret algorithms for encryption, signature, and hashing, we can get rid of excessive dependence on foreign algorithms.
Study on Watermarking Relational Databases

The use of digital watermarking to secure the content of database systems is a new research direction in information security. Until now, the majority of the work has been based on image, video and audio. However, because of the growing use of relational database systems, database watermarking has become a research subject. Which addresses the legal issue of database system copyright protection. In this paper, we have focused on the review of four relational database watermarking techniques proposed by researchers [R. Agarwal &amp; Jerry Kiernan, ZHU Qin, Brijesh B. Mehta, A. Al-Haj and Ali Hamadou]. The security of relational databases has been a great concern since the expanded use of these data over the Internet. Digital watermarking for relational databases emerged as a candidate solution to provide copyright protection, tamper detection, traitor tracing and maintaining integrity of relational data.