Reviewerly/exHarmony huggingface dataset:
Self-Organizing Deep Learning Model for Network Traffic Classification

Network traffic classification is one of the active research streams in the field of network management. Network traffic classification plays a crucial role in providing quality of service (QoS), management, forecasting future trends, and detecting potential security threats. A network operator must provide services based on QoS for each application in a network. Incorporating intelligence in network traffic systems plays a significant role in providing QoS. Deep learning prevents the need to select features by a domain expert as it automatically selects features through training. On concerning the above things, a self-organizing deep learning model for network traffic classification is proposed. In the proposed system, the features are extracted using deep learning models. The extracted features are then fed into the proposed self-organizing model. The self-organizing model is trained with the labelled data for clustering which in turn will cluster the unlabelled data. After further refinement, the cluster tends to update its centroid in the optimal position. Then, the process of dimensionality reduction takes place where the dimensions of the features are reduced based on the filter and wrapper methods. Finally, the performance of these methods is given to the classification algorithms which classify the features. The work would be further extended by developing a feature extraction model using deep learning which is expected to provide an efficient traffic classification while comparing with the state-of-the-art network traffic classification models.
Deep Anti-Aliasing of Whole Focal Stack Using Slice Spectrum

The paper aims at removing the aliasing effects of the whole focal stack generated from a sparse-sampled {4D} light field, while keeping the consistency across all the focal layers. We first explore the structural characteristics embedded in the focal stack slice and its corresponding frequency-domain representation, i.e., the Focal Stack Spectrum (FSS). We observe that the energy distribution of the FSS always resides within the same triangular area under different angular sampling rates, additionally the continuity of the Point Spread Function (PSF) is intrinsically maintained in the FSS. Based on these two observations, we propose a learning-based FSS reconstruction approach for one-time aliasing removing over the whole focal stack. Moreover, a novel conjugate-symmetric loss function is proposed for the optimization. Compared to previous works, our method avoids an explicit depth estimation, and can handle challenging large-disparity scenarios. Experimental results on both synthetic and real light field datasets show the superiority of the proposed approach for different scenes and various angular sampling rates.
Generative Adversarial Networks for single image with high quality image

The SinGAN is one of generative adversarial networks that can be trained on a single nature image.It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample.To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling.This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images.The low-resolution images usually contain much global information.Therefore, it can help the model to learn more global feature information from downsampled images.Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information.This can make the network learn more local details.Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image.A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.
Online teaching at the Universidad Veracruzana: Emerging strategies and challenges

In this paper, we briefly dissert past pandemics throughout history before addressing Covid-19. The objective is to describe the effects it has had on higher education and especially language, translation, and research teaching, as well as the challenges that have been faced with shifting from face-to-face lessons to online learning with help from information technologies. The advantages and disadvantages of this educational approach in a postgraduate context are reviewed, and the solutions that had to be implemented to offer quality education and avoid negative consequences for students' learning. This project explains the strategies applied by the authorities, professors, and students at the Universidad Veracruzana. It is a qualitative interpretative study in which the data was collected through semi-structured interviews. The most interesting findings were the writing and speaking improvements of the students, given the thorough review from their professors and peers. Moreover, they learned to construct their knowledge and become more autonomous in search of solutions to the problems during confinement. At the same time, the professors learned to operate multiple e-learning platforms so as not to stay behind. In conclusion, all involved learned to be more resilient and positively look at life, despite the obstacles.
An In-Depth Analysis of RFID Versus Barcode Scanning for Tactile Learning

In the world of object detection commercially available, Radio Frequency Identification (RFID) and Barcodes are two major, widespread applications. These are simple for a user and universally understandable. This paper compares the effectiveness of these two methods when applied to devices intended for tactile learning through the use of multiple objects with unique shapes and textures.
Implementation of K-Means Clustering Algorithm in Determining Classification of the Spread of the COVID-19 Virus in Bali

The COVID-19 virus or also known as SARS-Cov-2 is an infectious disease caused by the Coronavirus which attacks the human respiratory system. The COVID-19 case has affected all provinces in Indonesia, including Bali. There is a total of 7481 cases in Bali and this is due to the lack of understanding of the community towards the COVID-19 prone areas in Bali. Therefore, it is necessary to group the areas prone to COVID-19 in Bali. One of the clustering algorithms is K-Means, this algorithm uses several groups for the placement of some data with a partition system. The grouping will be carried out using data from the Bali COVID-19 Task Force website on September 18, 2020, using RapidMiner application. The results obtained divided Bali into 3 clusters with Denpasar as the center of the highest spread of COVID-19 in Bali as the red zone, then Badung, Buleleng, Bangli, Gianyar, and Karangasem in the yellow zone, and other districts in the green zone.
Open Science since COVID-19: Open Access + Open Data.

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A Dynamic Multi-Swarm Particle Swarm Optimization With Global Detection Mechanism

To overcome the shortcomings of the standard particle swarm optimization algorithm (PSO), such as premature convergence and low precision, a dynamic multi-swarm PSO with global detection mechanism (DMS-PSO-GD) is proposed. In DMS-PSO-GD, the whole population is divided into two kinds of sub-swarms: several same-sized dynamic sub-swarms and a global sub-swarm. The dynamic sub-swarms achieve information interaction and sharing among themselves through the randomly regrouping strategy. The global sub-swarm evolves independently and learns from the optimal individuals of the dynamic sub-swarm with dominant characteristics. During the evolution process of the population, the variances and average fitness values of dynamic sub-swarms are used for measuring the distribution of the particles, by which the dominant one and the optimal individual can be detected easily. The comparison results among DMS-PSO-GD and other 5 well-known algorithms suggest that it demonstrates superior performance for solving different types of functions.
Fraud Detection in E-Commerce Using Machine Learning

Customers rely heavily on decisions to purchase products either on commerce sites or in online retail outlets. Since these reviews are the game changers for success or failure in product marketing, reviews are used for positive or negative ideas. Improper reviews may also be referred to as false / fraudulent reviews or spam comments or false reviews. To downgrade or advance the item, resentful audits or phony surveys, which are tricky, are posted in the web-based business site. This outcome will prompt possible monetary misfortunes or bigger measure of development in business.So, the proposed system is design and developed in such way that it will detect fake, false and spam reviews for fraud detection using machine learning approaches like Sentiment Analysis, Support Vector Machine (SVM), Decision Tree algorithm, and N-gram model
Cybercrime's tendencies of the teenagers in the COVID-19 era: assessing the influence of mobile games, social networks and religious attitudes

Purpose The teenager community is the most affected community by cybercrime in the COVID-19 era. Increasing social networks and facilitating teenager access to the Internet have increased the probability of cybercrimes. On the other hand, entertainment such as mobile and computer games is top-rated among teenagers. Teenagers' tendency to cybercrime may be influenced by individual, parent, social, economic and political factors. Studying the impact of social networks, mobile games and parents' religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era is the primary goal of this paper. Design/methodology/approach The outbreak of COVID-19 caused a considerable change in the world and the lifestyle of all people. Information and Communication Technology (ICT) was also affected by the special conditions of this virus. Changes in ICT and rapid access to it have empowered individuals and organizations, and people have increased civic participation and interaction through ICT. However, the outbreak of COVID-19 has created new challenges for the government and citizens and may cause new crimes. Cybercrime is a type of crime that occurs in a cyber environment. These crimes range from invasions of privacy to crimes in which the offender vaguely paralyzes the macroeconomic. In this research, 265 students of high schools and universities are used for collecting data by utilizing a survey. Measuring actions have been done in all surveys employing a Likert scale. The causal pattern is assessed through a constructional equation modeling procedure to study the scheme's validity and reliability. Findings The outcomes have indicated that social networks have no significant relationship with teenagers' tendency to cybercrimes in the COVID-19 era. Mobile games have a mild effect on teenagers' tendency to cybercrimes in the COVID-19 era, and parents' religious attitudes significantly impact teenagers' tendency to cybercrimes in the COVID-19 era. Research limitations/implications Current research also has some restrictions that must be noticed in assessing the outcomes. First, sample research was selected from high schools and universities in one city. So, the size of the model is small, and the generalization of results is limited. Second, this research may have ignored other variables that affect the tendency of teenagers' to cybercrime. Future researchers intend to investigate the parents' upbringing system's impact on teenager's trend to cybercrime in the COVID-19 era. Future research can also examine practical factors such as parental upbringing, attitudes toward technology development and virtual addiction in the COVID-19 era. Originality/value In this study, teenagers' tendency to cybercrimes in the COVID-19 era is investigated, and a procedure is applied depending on a practical occasion. This article's offered sample provides a perfect framework for influencing parents' social networks, mobile games and religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era.
Ternary quantum codes constructed from extremal self-dual codes and self-orthogonal codes

&lt;abstract&gt;&lt;p&gt;In this paper, we consider $ S $-chains of extremal self-dual and self-orthogonal codes and their applications in the construction of quantum codes. Then, by virtue of covering radius, we determine necessary conditions for linear codes to have subcodes with large dual distances and design a new $ S $-chain search method. As computational results, 18 $ S $-chains with large distances are obtained, and many good quantum codes can be derived from those $ S $-chains by Steane construction, some of which improve the previous results.&lt;/p&gt;&lt;/abstract&gt;
Cross Your Body: a Cognitive Assessment System for Children

While many action recognition techniques have great success on public benchmarks, such performance is not necessarily replicated in real-world scenarios, where the data comes from specific application requirements. The specific real-world application that we are focusing on in this paper is cognitive assessment in children using cognitively demanding physical tasks. We created a system called Cross-Your-Body and recorded data, which is unique in several aspects, including the fact that the tasks have been designed by psychologists, the subjects are children, and the videos capture real-world usage, as they record children performing tasks during real-world assessment by psychologists. Other distinguishing features of our system is that it’s scores can directly be translated to measure executive functioning which is one of the key factor to distinguish onset of ADHD in adolescent kids. Due to imprecise execution of actions performed by children, and the presence of fine-grained motion patterns, we systematically investigate and evaluate relevant methods on the recorded data. It is our goal that this system will be useful in advancing research in cognitive assessment of kids.
Case-Based Task Generalization in Model-Based Reinforcement Learning

Model-based reinforcement learning has recently demonstrated significant advances in solving complex problems of sequential decision-making. Updating the model using the case of solving the current task allows the agent to update the model and apply it to improve the efficiency of solving the following similar tasks. This approach also aligns with case-based planning methods, which already have mechanisms for retrieving and reusing precedents. In this work, we propose a meta-learned case retrieval mechanism that provides case-based samples for the agent to accelerate the learning process. We have tested the performance of the proposed approach on the well-known MuJoCo dataset and have shown results at the level of methods using pre-generated expert data.
Remote English Teaching Resource Sharing Based on Internet O2O Model

Aiming at the problems of low success rate, delay, and high communication cost in distance English teaching resource sharing, this paper puts forward a method of distance English teaching resource sharing based on Internet O2O mode. Based on the model of distance English teaching resource sharing, this paper designs four processes: query, reply, resource substitution, and resource sharing optimization. Experimental results show that the proposed method can achieve high success rate of resource sharing, low latency, communication cost, and high transmission efficiency. Therefore, it is an effective method.
Credit Card Fraud Detection Using CNN

In financial transactions, credit cards are increasingly popular, while fraud is growing as well. Traditional methods are focused on regulations based on experts that ignore a variety of conditions and an extreme discrepancy between positive and negative samples to detect fraud behaviors. A feature matrix, a neural network of convolutions, is used to characterize large quantities of transaction data. To identify a group of secret patterns, a network is used. Any specimen Real-world scenario experiments. The large transactions of a big commercial bank show their superior effectiveness over other cutting-edge approaches.
Building City Dashboards for Different Types of Users

City dashboard websites are a common modality for bringing open-government philosophies into the public domain. Yet, there has been little research concerning the optimum design for city dashboards that takes account of users’ expectations and skills. Indeed, there has been minimal exploration of user-centered design (UCD) to improve the usability and utility of smart city technologies in general. This study sought to conduct a user evaluation analysis to inform a UCD approach to city dashboards. Interviews with different types of users were conducted that applied a protocol analysis to gain insight into user perspectives and experiences of city dashboards. Along with critical incident technique procedures, interaction data of critical significance to the user was collected and a content analysis was conducted. These qualitative data were used to determine representations of users, as identified through observed behaviors, attitudes, needs, and goals. Targeted-scope user experience personas for the design process were then constructed to represent and build empathy towards three potential users of city dashboard systems: novices, end-users, and advanced users. The collected user requirements and the personas formulated are underpinning the re-design of an existing city dashboard.
Moodle Innovation Learning Technology for Medical Education: From Theory to Practice

Background: COVID-19 pandemic has affected the medical and pharmaceutical education globally by disrupting traditional educational practices such as classroom-based teaching. This necessitated implementation of a distance learning via online platforms by medical and pharmacy schools around the world, including Ukraine.&#x0D; Aims: The short review aims at studying the global experience on use of innovative e-learning platform Moodle for distant medical and pharmaceutical education, figuring out its advantages and disadvantages for teaching medical/pharmacy students and healthcare professionals, and working out some recommendations for its further implementation in Ukrainian medical schools.&#x0D; Methods: For this short review, the authors relied only on accredited and peer-reviewed resources with free access articles available, such as Pubmed. The searches identified 32 full-textual content articles, of which 19 had been included in this paper and submitted to a narrative review.&#x0D; Results: The Moodle platform is successfully used for distant medical and pharmaceutical education worldwide, including both high and low income countries, to provide e-learning for medical/pharmacy students and healthcare professionals of different level of education, in the form of both online only courses and blended learning. The platform is a good alternative to conventional medical education with an ability to substitute hands-on teaching for video and audio materials. The potential issues related to its use include possible technical problems, i.e. insufficient technological support or Interment failure, and necessity for proper preparation of the medical and pharmacy teachers.&#x0D; Conclusion: E-learning via Moodle can be easily accommodated in the medical curriculum and offer an adequate alternative to the traditional teaching and learning activities, including both theoretical knowledge and practical skills acquisition. Its potential benefits clearly overweigh possible problems involved. The Moodle platform is recommended for further wider practical application for providing e-learning to medical and pharmacy students and healthcare professionals in Ukraine.
Design and Implementation of Pipelined AES Encryption System using FPGA

Nowadays, the data encryption became very important because of the usage of the data transmission in all the filed. The Advanced Encryption Standard (AES) that known as Rijndael algorithm is one of the most common encryption algorithms. The AES consists of 9 rounds in addition to the initial and final rounds that makes the AES consumes much time for encrypting the data. Of course the time consumption is considered one of the problems that face the information security. The more time the encryption system consumes to encrypt the data, the more chances increase for the hackers to break into the system. In this work, we find a new technique that can be used to increase the performance speed of the advanced encryption standard. The proposed algorithm methodology depends on the pipelined processing method for the processing time reduction. The paper includes a discussion of the design, the analysis and the implementation using Field Programmable Gate Array (FPGA) of the pipelined method to reduce the consumed numbers of clocks and speed up the processes. The AES is used to protect information and encrypt sensitive data and used in satellites, missiles, military application and other critical application. The paper describes the AES encryption system algorithm and the implementation of both the normal processing and the pipelined processing, and finally a comparison between the two algorithms.
All Optical Nonlinear Activation Function Based on Ge/Si Hybrid Micro-Ring Resonator

We demonstrate an all optical nonlinear activation function device based on Ge/Si hybrid micro-ring resonator. Three different types of nonlinear function are realized with threshold below 1mW and they can work well beyond 5MHz.
A Culture-Based Artificial Bee Colony Algorithm for Optimization in Dynamic Environments

A novel culture-based multiswarm artificial bee colony (CMABC) algorithm was proposed to address dynamic optimization problems. The historical experience of sub-swarms is preserved as cultural knowledge to guide the subsequent evolutionary process. Experiments were conducted on the moving peaks benchmark function. The results show that the CMABC algorithm was better than, or at least comparable to, the basic ABC algorithm, and other state-of-the-art algorithms.
Evaluating User Satisfaction Towards E-learning Platform During COVID-19 Crisis

The outbreak of the COVID-19 epidemic has spread worldwide causing Movement control order in Malaysia since mid-March 2020 however, the teaching and learning process in university still needs to proceed as usual.Hence universities improvised their teaching and learning process to fully online via an online platform.This study concentrates on student satisfaction as an end-user towards E-learning hence, the objective of this paper is to assess the acceptance of students; to evaluate the overall satisfaction; to identify challenges, and finally; to proposed improvements towards the implementation of the teaching and learning process through E-learning.Based on the standards population sampling of 95% confidence level with a 5% margin of error, the respondent has answers sets of five (5) section predeveloped questionnaires aiming at measuring end-user satisfaction towards E-learning platform.Results show that the information quality, system usefulness, system usage characteristics of E-learning is highly acceptable with overall satisfaction mean of 3.7 and SD