Reviewerly/exHarmony huggingface dataset:
Single Image Super-Resolution Using Feedback Attention Networks

Image super-resolution (SR) is a low-level vision task to recover HR images from their matching LR images. Recently, deep learning has provided a great boost to image SR tasks. In super-resolution tasks, obtaining better visual effects requires more accurate acquisition of high-frequency information. However, the input of low-resolution (LR) images contains a large amount of low-frequency information, which hinders the representational capability of networks and makes it more difficult to train. To address these issues, we propose a feedback attention network (FBAN) to better correct the features from LR input. Specifically, we use a constrained recursive neural network (RNN) to implement this feedback manner. Attention mechanism is added to the feedback block to make main network better extract high-frequency information. The attention mechanism consists of channel attention (CA) and spatial attention (SA). Extensive experimental results demonstrate the superiority of our FBAN.
Protecting Data by Improving the Performance of Controlling Expansion Method

Significant development of information technology has brought many advantages for people to communicate and transfer data more comfortable than before. Nevertheless, this easiness requires users to protect their data to prevent them from illegal access. Data hiding has been introduced to do this data protection; however, the quality of the generated stego data and the capacity of the payload are still the challenges. In this research, we work on these two problems by developing a method base on the Controlling Expansion. Here, the use of various base numbers has been explored along with the corresponding modulus values. The experimental results obtained by using a public database show that this proposed scheme can improve the original method’s performance. An increase of about 0.5 dB can be achieved by still maintaining the amount of the payload size.
FIR Filters for Phase Shifting and Differentiation

In this chapter we learn about special FIR filters not aimed at changing signal frequency content but focused on some special signal processing tasks. First we become familiar with the Hilbert filter which is shifting signal components in phase by minus 90 degrees. We will see that this very simple and seemingly not very important feature has a crucial significance for amplitude as well as phase and frequency signal demodulation. The very important role in this task plays the complex-value analytic signal, calculated by means of the Hilbert transform. We learn its definition, calculation, and application to signal demodulation. As an example, we demodulate signals with double-side-band (DSB) and single side-band (SSB) amplitude modulation (AM) as well signals with frequency modulation (FM). The second hero of this chapter is differentiation filter—filter calculating derivative of a signal. The signal differentiation is also very important in many application. We learn example of frequency demodulation.
Improved SMO Based on Perturbation Rate in Local Leader Phase

The paper, proposed a modified local leader phase of SMO based on perturbation rate and knowledge learn from experience of any spider monkey. “Improved SMO based on perturbation rate in local leader phase” and abbreviated as vSMO or variant of SMO is the name of proposed variant and approach gives more chance for travel or searching in the already searched area. To avoid poor divergence, paper allows incorporation of best known random direction for further improvement in perturbation rate that is, the amount of value by which perturbation rate changes on the basis of knowledge of any spider monkey which is obtained by experience during exploration of search space. Performance of this approach is tested over some bench mark functions out of which 26 functions including real world problems are showing that result is improved when compared with other methods.
AppPACK: A Packaging Model for Single-Purpose Lightweight Virtualization Environment

In recent years, container technology has caught the attention of the communities by its performance and compactness.Although the design of modern container tools (e.g., Docker and podman) serves as a single-purpose application provider, existing deployed containers still contain extra tools that are unnecessary for a single-purpose process.The existence of unnecessary files and tools is directly proportional to higher security risk.Besides, extraneous files often make the container heavier and slow down its performance.This paper introduces a novel Lightweight Virtualization packaging model for creating profiles for a single-purpose application from an existing multi-purpose container environment, called AppPACK.Specifically, the model can generate a package containing minified versions of images, kernel, and virtual machine profiles from a target application.The experiment results show that AppPACK can provide an image of 1.1 to 37 times smaller in size compared to the original version.The experiment on execution shows that using AppPACK profiles could fasten the booting process from 1.1 to 6 times compared to the non-AppPACK version.The comparison between AppPACK and previous approaches shows that proposed model can provide more compatible and smaller versions in most cases.
Proper Cost Hamiltonian Design for Combinatorial Optimization Problems: A Boolean Function Approach

Advanced researches on the variational quantum algorithms are actively conducted. In particular, the quantum approximate optimization algorithm (QAOA) is one of the promising variational quantum algorithms and can be applied to various graph-based problems, and is a promising algorithm that shows good performance even in small quantum computers. As is widely known, QAOA obtains the approximate solution via the expectation value of the cost Hamiltonian on the parameterized state. Therefore, in addition to finding the optimal parameters, the proper design of the cost Hamiltonian is important. This paper designs the cost function of the combinatorial optimization problem via Boolean function and maps it to the proper cost Hamiltonian. The proposed cost Hamiltonian design method is applied to the maximum independent set (MIS) and minimum dominating set (MDS) problems.
Fraud dalam Perspektif Islam

Islam regulates all the problems of its people, one of which is regulated by Islam, namely the relationship between humans. This study aims to try to reveal how cheating or cheating behavior is from an Islamic perspective. The investigation technique used in this study is a qualitative method with a literature review approach where the researcher collects some literature from both international and national journals. The data taken from these journals are then used as study material in writing this research. The results of the study illustrate that Islam has provided clues or signs of cheating or fraudulent behavior through the Koran and Hadith. From the results of the study, it is known that Islam has regulated all the problems of its people, especially the problem of fraud, Islam forbids its followers to do this. In addition, this research reveals that the practice of deception or what is known as cheating has existed for centuries, and Islam has clearly and emphatically provided a picture of how fraud occurs and how the impact of punishment on its followers. This research is also expected to contribute to knowledge related to fraud or fraudulent practices as well as a contribution to enlightenment for society.
Implementation of collaborative filtering algorithm with time attribute

Personalized recommendation means that the system recommends information and products that users are interested in based on the user's interest characteristics and purchase behavior. Aiming at the timeliness of the user's implicit feedback data, this paper uses the Sigmoid function and exponential weighted average to calculate the user's implicit feedback data, and converts the implicit feedback data into the user's preference for goods, then combine with the user-based collaborative filtering algorithm to obtain a recommendation list that meets the user's preferences, and complete personalized recommendations for user interests.
Data Mining based Hybrid Latent Representation Induced Ensemble Model Towards Fraud Prediction

The increasing growth of e-commerce and mobile payments has contributed to a rise in the effects of financial payment services fraud over the last few years. "At 2025, the losses in fraud around the world could amount to about $44 billion, according to McKinsey." Every year, trillions of dollars are lost from fraudulent card transactions. An efficient fraud detection system based on advanced machine learning techniques have gained excellent value in all Financial Institutions to reduce these losses. The proposed algorithm SLREnsemble (Stochastic Latent Representation Ensemble) is a hybrid model that uses deep neural network Autoencoders to obtain the latent representation of genuine and fraudulent transactions and these representations are given as an input to the balanced ensemble model. The technique is tested against other existing methods, i.e., Isolation Forest, Random Forest, and XGBoost algorithms, by various performance metrics such as accuracy, precision, and recall. The results prove that the SLREnsemble model is highly efficient in Fraud detection and performs well with minimal training data and yields good precision, recall and f1-score with high computational speed values.
APPLICATION OF TESTING TECHNOLOGY IN PROGRAM TRAINING

The purpose of the paper is to improve the methods of using test technologies in the educational process and to develop an optimal schedule for conducting tests in higher education when teaching programming languages. The article considers the advantages and disadvantages of test technologies. The author investigates the effectiveness of one-time and step-by-step testing and studies the role of testing at different stages of the educational process. In the course of the study, the author used written and computer forms of testing. The paper shows that step-by-step testing is more effective than one-time testing. The article identifies the expediency of testing that precedes the implementation of practical work. The results of the study allow us to develop an optimal schedule for conducting tests, which contributes to improving the quality of learning material and increasing students’ motivation.
Data Transmission using Crypto-Steganography

In today’s world as the number of people who have means of approach to the internet or technology is growing rapidly, so the need to conceal the sensitive data from attackers is of vital importance. On daily basis copious amount data transmissions take place which need to be secure and care needs to be taken that the sensitive data does not fall in any wrong hands. Various systems have been actualized previously in which single tier security is provided which is not that coherent and the systems are quite vulnerable. In order to overcome this impediment, the proposed system in the paper consists of 2 tier security which includes keyless cryptography and steganography. In Cryptography plain text is converted into cipher text and secrete message is embedded at the sender side which is decrypted at the receiver side. Whereas steganography is the process of hiding data behind the image. The first phase in the proposed systems includes cryptography which uses random logic for encryption and the second phase includes steganography which uses GCD logic. All the algorithms used in this system are keyless which in turn adds up to more security and makes the system light weighted.
A Total Variation Model for Multiplicative Noise Removal and Blind Inpainting

Multiplicative noise and artifacts removal are two important tasks in the field of image processing. In some applications, multiplicative noise and intensity missing always exist at the same time. In this paper, we introduce a new variational model based on total variation and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm for simultaneously removing the multiplicative noise, estimating the location of missing pixels, and filling in them. To be specific, we use the total variation to regularize the estimated image, and use the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm to make the missing pixels to be sparse. Moreover, based on the Gamma noise assumption, the data fidelity term is given by a new forward description of the degraded process. Then, for solving the proposed model, we develop an algorithm by exploiting the alternating direction method of multipliers (ADMM). The experiments validate that the proposed method can effectively restore the synthetic and real images damaged by the multiplicative Gamma noise and artifacts, and meanwhile estimate the location of missing pixels.
Studying attitudes towards vaccine hesitance and California law SB 277 in online discourse: A dataset and methodology

This article presents data that are further analyzed and interpreted in “Shouting at Each Other into the Void: A Semantic Network Analysis of Vaccine Hesitance and Support in Online Discourse Regarding California Law SB277” [1]. This research modified snowball sampling, a technique usually used to generate chains of informants that illuminate the structure of social networks, to collect digital documents following a chain of web links and recommendations, thus illuminating the underlying social, technical, and linguistic structure of online discourse. The resulting documents were manually coded according to the attitude towards vaccines they represented and/or the position they took with regard to California Senate Bill 277, a vaccine mandate policy that banned all nonmedical exemptions from school immunization requirements. Each attitude category, as well as the dataset as a whole, was subjected to quantitative linguistic analysis to identify key words and phrases in the data according to the frequency with which they appeared. A combination of that technique and semantic network analysis were used to generate clusters of related words that could be used for qualitative and narrative analysis, as detailed in the companion paper. The data collection and analysis processes described here will be of use to researchers conducting mixed-method analysis of online discourse who want their data to reflect the potential information and digital resources available to individuals who attempt to inform themselves about a particular topic using Internet searches. The data presented here could be useful for anyone seeking deeper insight into the linguistic and narrative patterns surrounding online debates about vaccination, controversial government policies, or both.
From Displacement to Entrepreneurship

The violence that hits Colombia for more than 50 years forced many people, families, and communities to leave their original territories and seek refuge in the principal cities, especially in Bogotá, where displaced people from various regions of the country arrived. Victims of this situation, the Wounaan-Nonam community, were relocated in the periphery of Bogotá, under challenging conditions, some without the necessary infrastructure to live with a minimum quality of life standards, and in a completely different context and culture than the one they were used to live. This research seeks to help artisan women for developing production processes around Wérregue products. As a result, this community will improve their productivity, structure their organization, define marketing strategies, and promote their sustainability and growth while protecting and respect their cultural essence and heritage to generate higher and sustainable income for the artisans.
Fuzzy C-means measurement of mass base on image features

Mass of an object is an important characteristic for quality assessment. However, in some cases, it is hard to measure the mass of objects with instruments directly. In this paper, we proposed a novel method based on Fuzzy c-means (FCM) to measure the mass of an object by analyzing the deformation degree in a grid pattern. In the spatial field, thin plate spline (TPS) algorithm was adopted to calculate the minimum deformation bending energy in order to give a quantitative analysis of the weight; In frequency domain, the Fast Fourier Transform (FFT) algorithm was used to calculate the spectrum within a deformation frequency area before and after the change of grids, from which the relationship between weight and spectrum was investigated using FCM algorithm. Two different equations evaluated by the above two methods were proposed in order to calculate the mass of an object. Both of them showed a high level of explanatory power of R2 (R2 = 0.9833 and R2 = 0.9698, respectively). The equations were then used to determine the estimated mass. Estimated and measured values were plotted against each other. A high correlation (R2 = 0.9833 and R2 = 0.9698, respectively) was found between actual and calculated mass. Finally, Bland-Altman plot was introduced to access the agreement of the calculated mass and the actual mass. The average bias was –54.408 g and –0.007 g for spatial domain method and frequency domain method, respectively. Theoretical analysis and experiments were performed to verify the effectiveness of our approaches.
Trend analysis of total rainfall events with light and heavy intensity for the 1976 – 2019 periode in Majene District – West Sulawesi

Abstract The increase of rain events due to climate change can affect local activities in several sectors such as agriculture, trade, infrastructure, etc, so climate information is importantly considered for operational sustainability. Therefore, this research is conducted to determine trend of changes in total rainy days and tendency of rain which refers to light rain &lt;5 mm/day and heavy &gt;30 mm/day every month in Majene based on Majene Meteorological Station data during 1976 – 2019 using linear regression method. Besides this research aims to provide information to related parties to consider policies as anticipatory and mitigating steps for natural phenomena to achieve effective activities. Results of analysis indicated a tendency of increasing trend in total of rainy days, the number of light and heavy rainy days in certain months. Based on rain posts, the increasing number of rainy days is dominantly followed by light rain events. This study informs that climate change has occurred in Majene, namely the increasing number of light rainy days. The information in this study can be used as knowledge to readers and consideration in certain policies, that the light rain happens a lot, but it cannot be denied that heavy rain will occur at any time unexpectedly.
The Concept of Using AR and VR Technologies in the Vocational Training System in Robotics and Automation

Development of automation and robotics causes a need for implementing an innovative approach towards teaching. It is especially important for vocational education, as for this an expensive equipment is needed to train within real-life setups. Thus, AR and VR technologies give an opportunity to decrease costs, while keeping a high level of course-taught skills and knowledge. The paper consists of an overview of possible applications of virtual and mixed realities for education purposes and a presentation of MILAN project. This involves a description of key ideas for skill-transfer based on e-learning platform using various ICT solutions; VR and AR among others. Also, already implemented VR methods in two robotics-training cases are presented.
Weibo Sentiment Classification Based on Two Channels Text Convolution Neural Network with Multi-Feature

Nowadays, Internet platforms such as Weibo have marked a new era for the public to express their attitudes, opinions and sentiments on the government's policies or various social phenomena. The analysis of those ideas and sentiments can provide practical significance to promote the governance of cyberspace, it is also very helpful to observe and warn cyber events. Taking Sina Weibo as the object, most of the research ignores the combination of sentiment prior knowledge and user information. Therefore, the accuracy of sentiment classification is affected. In order to make full use of the rich information on social networks, the two channels text convolution neural network (TC-TextCNN), which combines multi-feature, is proposed. By improving the algorithm of the word embedding, the part of sentiment information is enhanced by sentiment dictionary. To supplement traditional features, this model construct user features according to attributes and behaviors of users. TC-TextCNN can learn semantic features, sentiment features and user features at the same time. The effectiveness of sentiment word embedding and user features has been proved by experiments. Experiments proved that this method achieves better results on classifying sentiment. And the difference of sentiment distribution under different cyber events is verified by empirical analysis, which provides a reference for the early warning of cyber events.
A VAE Conversion Method for Heterogeneous Data Inputs to Create Uniform Outputs for Diagnosis

With the development of Industry 4.0 and the Internet of Things, in order to provide a more effective and reliable monitoring system, industrial machinery is equipped with multiple sensors to form a multi-sensor system that provides high quality and large amounts of data for diagnosis. However, different components of machine may have different features, and the same type of sensor has different characteristic outputs due to different sensing positions, resulting in heterogeneous data. Therefore, it is necessary to establish a complex data preprocessing process to train an effective detection model. In addition, the trained model is immutable. When the configuration of the sensors is changed, a lot of time must be spent to retrain the model. It is difficult to efficiently adjust the model according to the actual situation. In view of data heterogeneity and model immutability of multi-sensor systems, this study proposes an architecture named two-stage anomaly detection system. In the first stage, the data conversion model called Converter is proposed in this paper to convert heterogeneous data to uniform, thus making multi-sensor diagnostic systems easy for training. The second stage considers the relationship among data input and integrates all the unified data for accurate diagnosis of the entire system. The experiment of this study uses the IMS rolling bearing data set to verify the effectiveness of this architecture. The results show that the two-stage architecture can effectively perform anomaly detection and help to deal with heterogeneous data and model variability in multi-sensor systems.
Security monitoring method for ICS based on information gain ratio and maximum entropy model

In today's information age, in order to meet the requirement of high efficiency outlier detection in the production process of industrial control system, the intrusion characteristics are analyzed and warned in time to prevent from happening. An intrusion detection scheme (IGR-MEM) based on information gain ratio feature selection and maximum entropy model is proposed. The collected industrial control data are normalized and a new information gain ratio feature selection method considering feature correlation and redundancy is proposed to choose the best feature subset from the network connection data. According to the extracted training sample feature subset, the maximum entropy model is devoted to construct the classifier, and finally the trained classifier is applied for intrusion detection. The test results express that the IGR-MEM scheme is able to select the best feature collection, improve the detection efficiency, and improve the accuracy of ICS security monitoring and reduce the false alarm rate compared with other algorithms.