Reviewerly/exHarmony huggingface dataset:
An Adaptive Oversampling Method for Imbalanced Datasets Based on Mean-Shift and SMOTE

Class imbalance is a challenge in different actual datasets, where the majority class contains a large number of data points, and the minority class contains a small number of data points. Class imbalance affects the learning process negatively, resulting in classification algorithms’ ignorance of the minority class. To address this issue, various researchers developed different algorithms to tackle the problem; however, the majority of these algorithms are complex and generate noise. This paper provides a simple and effective oversampling technique based on the mean-shift clustering algorithm and using the synthetic minority oversampling technique (SMOTE) of selected clusters. We conducted several experiments to compare the performance of our technique with different algorithms mentioned in the literature on three common datasets. Experimental results indicate that our technique performs better in synthesizing new samples and improves support vector machine (SVM) classification performance on imbalanced datasets.
Automated Cheque Processing Through Data Verification and Siamese Networks

Bank cheques are still majorly used for financial transactions all over the world. A large number of cheques are processed manually on a daily basis, thereby requiring a lot of time, money and human effort. In such a manual verification, information like date, signature, amount present on the cheque has to be physically verified. This paper aims at finding a solution for processing cheques which increases the efficiency of this process, while minimizing human intervention. The service was hosted locally on a webpage, we first accepted the cheque image from the user, passed it to OpenCV which returned the various parts of the cheque which were then passed to Google Vision API to convert them into text, but MICR code was passed to tesseract OCR. After the successful extraction, the details were verified from the information in the SQLite database and signature was verified using the model trained in Jupyter Notebook.
The Effect of Physical Education Teachers" Autonomy Support on Students" Perception of Errors and Classroom Engagement

The purpose of this study is to investigate the effect of physical education teachers" autonomy support on students" perception of errors and classroom engagement. In order to achieve the purpose of this study, middle and high school students in Gyeonggi-do were selected as the population, and 342 students were selected through convenience sampling. The collected data were analyzed using the statistical program SPSS Win Ver 23.0 and multiple regression analysis. The results of the analysis are as follows. First, physical education teachers" autonomy support had a statistically significant effect on the students" perception of errors(reflecting on errors, learning errors, and trying by accepting the risk of errors), while it showed no significant effect on the factor of the pressure of errors. Second, in a physical education class, the students" perception of errors had a statistically significant effect on classroom engagement, while the pressure of errors, one of the factors of perception of errors, did not have a significant effect. Finally, physical education teachers" autonomy support showed a statistically significant effect on the students" classroom engagement.
LiDeR: Lightweight Dense Residual Network for Video Super-Resolution on Mobile Devices

Video is now an essential part of the Internet. The increasing popularity of video streaming on mobile devices and the improvement in mobile displays brought together challenges to meet user expectations. Advancements in deep neural networks have seen successful applications on several computer vision tasks such as super-resolution (SR). Although DNN-based SR methods significantly improve over traditional methods, their computational complexity makes them challenging to apply on devices with limited power, such as smartphones. However, with the improvement in mobile hardware, especially GPUs, it is now possible to use DNN based solutions, though existing DNN based SR solutions are still too complex. This paper proposes LiDeR, a lightweight video SR network specifically tailored toward mobile devices. Experimental results show that LiDeR can achieve competitive SR performance with state-of-the-art networks while improving the execution speed significantly, i.e., 267% for X4 upscaling and 353% for X2 upscaling compared to ESPCN.
Deep Learning for Secure Transmission in Wireless Communication Networks

Advancements in information and communication technologies have contributed significantly to the optimization of next generation wireless communications networks. Moreover, wireless communications play a huge role in electronic warfare and the emergence of new technologies continue to provoke a corresponding revolution in battlefield operations. Nevertheless, the widened attack surface that result from the increased wireless networking of battlefield devices lure attackers like jammers and eavesdroppers who seek to explore the vulnerability of the network through the physical layer. Consequently, it is imperative to direct research efforts not only towards advancing spectral and energy efficiency but also secure wireless communications. In order to address the challenges, we leverage on the recent developments in the use of machine learning (ML) for wireless communications to propose a novel approach for secure wave-form transmission in battlefield operations. Specifically, we use the learning-based end-to-end representation of communication systems where the transmitter and receiver are represented by two deep neural networks (DNN). The goal is to prevent the eavesdropper from accessing the communications between the transmitter and the receiver without necessarily a feedback mechanism as in the conventional communication system. In comparison to traditional optimization methods that are usually iterative in nature, the advantages of employing deep learning (DL) includes its ability to address the challenges of statistically inclined methods. Using simulations, we compare the proposed method with other methods to ascertain its credibility.
SCPS:A Secure and Copyright-Preserving System for IIoT Based on Hyperledger Fabric

Data, a key asset in Industrial Internet of Things(IIoT), has fueled the emergence of a new data trading in-dustry, which can be collected and traded for better development of IIoT. However, copyright protection is not considered in traditional transactions, resulting in the loss of copyright control of the data after the data was sold. A large number of piracy activities will reduce the enthusiasm of data owners to collect data. The solution we proposed was to use blockchain and smart contract to achieve a decentralized and secure copyright protection scheme. The Trusted Execution Environment(TEE) helped our system realized the piracy tracking. To be specific, our solution effectively achieves a secure and copyright protection system of IIoT data, we realize the real simulation on Hyperledger Fabric shows the good performance of our system.
Action recognition based on parallel convolutional recurrent neural networks

To address the problem that a single feature of a video is insufficient for the representation of video motion information, we propose a parallel convolutional recurrent neural network-based action recognition. In this the parallel convolutional recurrent neural network, RGB image features and human joint point skeleton features were put into the CNN and RNN+LSTM, respectively. Furthermore, two features were connected into a joint spatio-temporal feature vector for finally action recognition. The experiment al results show that the action recognition accuracy of this paper method on UCF101 dataset is better than the current mainstream action recognition methods, which verifies the effectiveness of this method on action recognition.
Pedagogical monitoring as a factor in the effectiveness of the discussion training of future engineers based on informatization tools

The problem of increasing the efficiency of debating training of students of technical universities by creating a competitive environment with the help of a pedagogical monitoring system based on a publicly available network table of progress is considered. A hypothesis has been put forward, according to which the use of pedagogical monitoring based on modern informatization tools makes it possible to update the motives of social positioning among students and increase the final effectiveness of the formation of their debating competence. A theoretical analysis of modern approaches to pedagogical monitoring is carried out and it is concluded that in the conditions of the information society and a competence-based approach to education, it should perform not only a diagnostic and corrective, but also a stimulating and motivational function. Using the method of subjective assessments based on our own questionnaire and non-parametric statistical tests (calculation of the contingency coefficient based on Pearson’s Chi-square), the results were obtained confirming the increase in the effectiveness of the formation of debating competence in almost 40% of the subjects when using the pedagogical monitoring system developed by us and proving the hypothesis put forward. In conclusion, it is concluded that it is expedient to further study the problem through a targeted pedagogical experiment aimed at identifying the degree of improvement in the performance indicators of debatable training of students of technical universities when using pedagogical monitoring based on a publicly available network table of progress and modern informatization tools.
Understanding the Jenkins Pipeline

In the previous chapter, you learned how to create a Jenkins job that will be triggered automatically. You automated a process of the Java Calculator API release using a free-style job. This job starts by pulling the latest code from the master branch and performs different build lifecycle phases on it, like compiling, unit testing, packaging, and deploying to the artifact repository. This chapter explains an important Jenkins concept—the pipeline.
Immersive media experiences

An introduction to the QUALINET White Paper on Definitions of Immersive Media Experience (IMEx) [1].
MPEG column: 131st MPEG meeting (virtual/online)

The 131st MPEG meeting concluded on July 3, 2020, online, again but with a press release comprising an impressive list of news items which is led by "MPEG Announces VVC - the Versatile Video Coding Standard". Just in the middle of the SC 29 (i.e., MPEG's parent body within ISO) restructuring process, MPEG successfully ratified - jointly with ITU-T's VCEG within JVET - its next-generation video codec among other interesting results from the 131st MPEG meeting.
nteract

Writing source code for programs with lightweight text editors or fully featured integrated development environments is considered the main method of programming. Notebooks, however, are an extremely practical tool. In contrast to IDEs, projects are set up more easily and they allow for running programs in a read-eval-print loop (REPL) environment. The Jupyter Notebooks Quick Start Guide [1] describes notebook documents as "… both human-readable documents containing the analysis description and the results (figures, tables, etc..) as well as executable documents which can be run to perform data analysis." Basically, markdown text can be mixed with program source code in a sequence of sections, each dedicated to either programming or description and documentation. Source code sections can be executed and the output is appended to the section, even formatted in the form of graphs, diagrams, or tables. REPL and notebook based environments have proven to be useful in many scenarios including when exploring new libraries and frameworks, to prototype code, or as an educational tool to create interactive lecture material. A prominent example is Jupyter, which is the highly successful project behind Jupyter Notebooks and the recent JupyterLab, i.e. web-based systems to run and share notebooks that contain code, equations, data visualizations, and data exploration and narrative text.
Sociocultural conditionality of change trends in school geographic education content

The article reveals the features of sociocultural conditionality and trends in the development of the content of geographical education in modern conditions. The components of the content of geographical education and their characteristics are considered. The format of the implementation of the content of geographical education in the line of geography textbooks &amp;quot;Polar Star&amp;quot; is shown.
A Framework for Unknown Traffic Identification Based on Neural Networks and Constraint Information

Nowadays, traffic identification is becoming increasingly important in network security. But in practice, we often encounter unknown traffic, in which we do not know its specific type, and makes it very difficult to manage and maintain network security. The ability to divide the mixed unknown traffic into multiple clusters, each of which contains only one type as far as possible, is a key point to tackle this problem. In this paper, we propose a framework for unknown traffic identification based on neural networks and constraint information to improve the clustering purity. The framework consists of two main innovations: (1) It uses neural network methods to reduce the dimensionality and select features of network traffic. (2) It analyzes the constraint information of traffic and uses this information to guide the process of identification. To verify the effectiveness of the framework in this paper, we make contrast experiments on two real-world packet traces respectively. Through our experimental results, we find that the maximum clustering purity of our framework in this paper can reach 96.10% on the traces of Internet Service Provider (ISP) and 91.89% on the public traces. Experimental results show that the proposed framework is more effective than Gaussian Mixture Model (GMM).
Development of the Biological Sensing Head Mounted Display

Measuring biometric information helps us estimate the users’ excitement degree and their negative and positive emotions. By measuring a person’s biometric information while experiencing the virtual reality (VR), it is possible to interactively change the content according to the estimated emotional state of the person. However, the hassle and discomfort of wearing the sensor interferes with the VR experience, and the body motion caused by the VR experience prevents accurate measurement. Therefore, some studies have developed devices that incorporate biometric measurement sensors into the head mounted displays (HMDs). Since we use HMDs by pressing them against our faces, biometric sensing by HMDs is resistant to body movements and can reduce the discomfort of sensor attachment. This paper introduces our research on HMDs with embedded sensors and our previous study as part of this project. This paper introduces the various biological sensing HMDs including our research and discusses VR applications using those HMDs.
Human Pose-Based Activity Recognition Approaches on Smart-Home Devices

With the gradual improvement of the intelligent degree of smart-home devices, its popularity is also multiplying. These devices dramatically improve the richness of available data, including a large number of indoor family life visual data. At the same time, it also has higher requirements on effectively using these data and further improving the intelligence of smart-home devices. In this paper, we mainly verify the performance of frameworks and feasibility of deploying the human activity recognition models when the computing power of edge computing devices is limited. It includes typical deep learning methods, CNN and GCN-based recognition methods, and a single activity judgment recognition method based on CTW. We also analyze the help of different data preprocessing steps in improving time efficiency and accuracy. In addition, a human activity dataset is built based on the actual home fitness equipment. The experimental results verify the feasibility and effectiveness of deploying the activity recognition model on IoT devices with limited computing capability.
Amharic Language Hate Speech Detection from Facebook Image Post Using BiLSTM + Dense

In recent years, there has been an increase in the spread of hate speech on social media platforms such as Facebook. Hate speech spreads on Facebook via video, text, or text-image. As a solution to these problems, we proposed hate speech detection system for Amharic language based on deep learning to detect hateful text-image from facebook. The research began by manually collecting text-images posted by Facebook users, followed by text extraction, data annotation, text preprocessing, data splitting, feature extraction, model building, evaluation, and testing. Text extraction from posted images is done using Abyssinia-OCR, MetaAppz, and Amharic-OCR. The text preprocessing stage consists of removing unwanted characters, normalizing characters and removing stop words. The dataset is divided into three parts using StratifiedKFold: the train set (80%) to train models, the validation set (10%), to evaluate models, and the test set (10%). The embedding layer is used to transform the preprocessed texts into vectors. The vectors are then fed to deep learning models built based on BiLSTM, BiGRU, and Dense Algorithms. After many experiments, the best model with the best hyperparameters is selected. The proposed system detected Amharic hateful text-image posts on Facebook with an accuracy of 81%.
Spell Painter: Motion Controlled Spellcasting for a Wizard Video Game

Motion controlled video games were popular with the release of the original Nintendo Wii, but that popularity faded over time as new peripherals were released. While the Wii’s success demonstrated the interest that customers have in motion controls, motion controls too often rely on expensive external hardware. In this work, we present Spell Painter, a fully motion-controlled spell dueling game that can be played with only a webcam. We present the game and its implementation, as well as two preliminary evaluations, which suggest the system has an accuracy between 80%–90%.
Usability Study of Museum Website Based on Analytic Hierarchy Process: A Case of Foshan Museum Website

With the development of communication technology and network, museum websites have become an important way for people to appreciate cultural relics and understand the urban culture. The museum website is the forerunner of the physical museum, and the usability of the website greatly affects the user’s experience. To systematically study the influencing factors of museum website usability, this paper used the Analytic Hierarchy Process (AHP) to construct a museum website usability evaluation system, and took the Foshan Museum website as the research object, using the classic usability testing method to evaluate the website usability, collected objective performance indicators, behavioral data, and subjective evaluations of participants. A total of 10 university graduate students participated in this experiment. The experimental results showed that the current website of Foshan Museum had problems such as confusion of information structure and lack of key information. The hierarchical structure of the website was optimized using the Hierarchical Task Analysis (HTA) method. Finally, the usability test was repeated, and the improvement of the website usability was verified through data analysis. This study also draws some general conclusions, which provide some reference value for the same type of website optimization in the future.
Customer Hierarchy of Needs: Customer Centric Approach to Agile Product Development

Oftentimes in Agile product development, the MVP (minimum viable product) is defined and agreed upon for development to release product quickly to learn directly from customers. As the team develops and works through the requirements for MVP, scope creep can happen; requirements change; or new customer requests come in which may change the direction of the work. When this occurs, the instinct of the team is to “react” to the changing demands and can cause the team to lose focus of what the original customer need was. To ensure that the development team is keeping the customer at the focal point of the work, the Customer Hierarchy of Needs was developed. The Customer Hierarchy of Needs method is an effective way to define and prioritize features and functionality for a product through the lens of what the customer may need or want based on their existing level of need. In this paper, we will explain the customer hierarchy of needs method, how it was applied to a project at Crown Equipment Corporation, lessons learned by the development team, and the benefits of utilizing this method.
Multilingual China-Related News Identification Framework Based on Multiple Strategies

In order to overcome the high recall but low accuracy of China-related news identification methods based on dictionary or machine learning, a China-related news identification framework for multilingual news is presented by using weakly supervised learning and multi-task learning. On the one hand, the framework uses XLM (Cross-lingual Language Model Pretraining) as a pre-trained language model to identify the China-related news for multiple languages, and uses the China-related dictionary for weakly supervised learning and completes the China-related word identification. On the other hand, we utilize pseudo-label technology to generate corpus, the pseudo-label technology is used to generate corpus. Then, we screen the pseudo-label texts with high confidence to form the training corpus together with the original data for training. The performance of the proposed framework in the task of China-related news identification with three languages is greatly improved compared with other methods. The average F1 score on the three corpora is 89.36%, which is 1.32% higher than that of the original XLM model. The results demonstrate that the tasks of pseudo-label technology and the China-related word identification in this framework can make the China-related news identification achieve better effect.
A Systematic Review About Quantitative Metrics for the Usability Evaluation of Retail E-commerce Web Sites Based on Open Source Platforms

The fast-growing e-commerce has created a new way to do transactions globally, as a result, more business has to provide online stores to maintain themselves in this new competitive environment. Given that context, it is understandable how much acceptance open-source platforms had received for new businesses, with their lower costs and open community support. Facing this growth in the e-commerce domain, the relevance of usability is even more significant. For this reason, metrics that can be used to determine whether a software product meets the appropriate level of usability are needed. However, the traditional metrics fail to cover all aspects that these open-source platforms. Because of that, this article presents a systematic review to identify metrics that can measure the degree of usability. It also shows the most reported aspects that are needed to measure in e-commerce websites based on open-source platforms.