Reviewerly/exHarmony huggingface dataset:
Metaverse Technologies and Applications: A Study

An interconnected network of 3D virtual worlds is the metaverse. As the virtual environment enables opportunities for extremely immersive and engaging experiences, interest in the metaverse has been expanding globally. This magical development in the metaverse enables new technologies to be integrated and form more advanced tools for a more realistic user experience. To completely meet the expectations of an immersive metaverse, many building blocks are required, such as edge computing, augmented reality, blockchain, and artificial intelligence. In this paper, we have discussed the technologies and applications of the metaverse. We have presented use cases and challenges that the metaverse is currently dealing with, as well as the important advantages of an interconnected world with the help of the metaverse
Performance Analysis of Nature Inspired Optimization Based Watermarking Schemes

In the digital era, nature is an incredible and massive source of motivation for learning hard and complex issues in computer science. Nature is a mother of learning because it shows various dynamic, powerful, complex, and exciting concepts. These algorithms are basically designed for solving various optimization problems. These algorithms always provide optimal results for the problem. In a few decades past, many researchers have introduced a large number of nature-inspired algorithms. Few natures inspired algorithms are more effective and useful comparatively other optimization algorithms to provide optimal results with watermarking techniques such as SVD (Singular Value Decomposition), DWT (Discrete Wavelet Transform), and DCT (Discrete Cosine Transform). This paper presents a short description about optimal digital image watermarking algorithms and applications of nature-inspired algorithms: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) & Firefly (FA). The primary purpose of the review is to acknowledge a comprehensive analysis of different nature-inspired algorithms based on their source of inspiration, characteristics, fundamentals, and implementations where these algorithms are properly executed to obtain the optimal solutions for a digital watermarking strategy for digital medical images.
Fake Image Dataset Generation of Sign Language Using GAN

The massive boost in the technology industry proves as a boon for data science researchers. In the past few decades, new emerging advancements in the field of ML, Deep learning, and image pre-processing makes it possible to automate the task of different fields which required humans rather expert humans’ involvement. Since the advent of neural networking, there has been an enormous demand for acquiring information and generating datasets to support various research projects and to train module work for supporting artificial learning, but due to the lack of diversity in datasets, there are major hurdles during data preparation. There is a need to develop a system that could provide the visual dataset for this communication learning for machine algorithms. To overcome this bottleneck of the neural network, GAN (Generative Adversarial Networks) were created to acquire fake data with the objective of anonymizing users’ information to generate a huge stack of data representations. But working on GAN is not an easy task because it requires a deep understanding of deep learning and image pre-processing. In this paper, a dataset is included to support work for the deaf community which uses sign language to communicate. The task is to read hand signals by detecting the movements and visualizing the communication mode through movements of fingers and to achieve that, personal photos have been captured which are provided as input samples to GAN based model. The various requirements of future work in the same field will require a vast amount of hand sign data which is lacking in the current domain as there have been limitations with different backgrounds, shortage of volunteers, and different gestures to portray generalized reading and storage of such data in a variable format of hand signs have been raised as an issue by many research workers. This data exists in mere thousands and scrapped way over the digitalized media so a methodology is provided to resolve this problem through a tool that will create as many amounts of images datasets as required and, this will provide a healthier feed for machine training that will support future as well as current data requirements for work going on for the deaf community.
CHED: A Cross-Historical Dataset with a Logical Event Schema for Classical Chinese Event Detection

Event detection (ED) is a crucial area of natural language processing that automates the extraction of specific event types from large-scale text, and studying historical ED in classical Chinese texts helps preserve and inherit historical and cultural heritage by extracting valuable information. However, classical Chinese language characteristics, such as ambiguous word classes and complex semantics, have posed challenges and led to a lack of datasets and limited research on event schema construction. In addition, large-scale datasets in English and modern Chinese are not directly applicable to historical ED in classical Chinese. To address these issues, we constructed a logical event schema for classical Chinese historical texts and annotated the resulting dataset, which is called classical Chinese Historical Event Dataset (CHED). The main challenges in our work on classical Chinese historical ED are accurately identifying and classifying events within cultural and linguistic contexts and addressing ambiguity resulting from multiple meanings of words in historical texts. Therefore, we have developed a set of annotation guidelines and provided annotators with an objective reference translation. The average Kappa coefficient after multiple cross-validation is 68.49%, indicating high quality and consistency. We conducted various tasks and comparative experiments on established baseline models for historical ED in classical Chinese. The results showed that BERT+CRF had the best performance on sequence labeling task, with an f1-score of 76.10%, indicating potential for further improvement (The CHED data is released on https://github.com/lcclab-blcu/CHED ).
Communication &amp; Collaboration with Contributors in an Open-Source Organization

&lt;em&gt;Zhiam Kamvar is Lesson Infrastructure Technology Developer at The Carpentries (Software Carpentry, Data Carpentry &amp;amp; Library Carpentry), an open global community teaching the skills &amp;amp; perspectives to turn data into knowledge.&lt;sup&gt;1&lt;/sup&gt;&lt;/em&gt;&lt;em&gt;Maëlle Salmon is a Research Software Engineer with rOpenSci.&lt;/em&gt;&lt;em&gt;In this post they compare their experiences in the two distinct organizations The Carpentries and rOpenSci.&lt;/em&gt; At rOpenSc
Prediction Calculation of PT. Indofood Sukses Makmur Tbk. Stock using R Studio with Autoregressive Integrated Moving Average (ARIMA) Method

PT. Indofood Sukses Makmur Tbk is one of the consumer stocks with a parent company, namely PT. Indofood Sukses Makmur Tbk (INDF) is also in the consumer sector. In 2020, the impact of the coronavirus pandemic will be felt by the public and the government, one of which also has a significant effect on the economic sector. Macro companies show stock prices dropping drastically in early 2020 due to the pandemic. And that's where investors are tempted to buy shares. However, until now, the price of macro companies' claims, including INDF's shares, still fluctuates. So it is difficult to determine the future stock price. Therefore, research is needed to predict INDF stock prices in the future. This study aims to provide information about INDF stock prices in the future based on prediction results which investors can then use to read INDF stock charts in the future so that they do not experience capital loss. This research uses R Studio with Autoregressive Integrated Moving Average (ARIMA) method. Based on the research method carried out in input and data processing, checking stationarity, model specifications, parameter estimation, residual analysis, and forecasting, the results obtained regarding the prediction of INDF stock prices show fairly accurate results. This can be seen from the results of stock price predictions in February – April 2021 with the actual data available. Figures from the actual data are still included in the upper and lower limits of the predicted results.
The Technology Acceptance of Intelligent Silaturrahmi-based Collaboration Gamification Mechanic (ISb-GM) in Small Medium Enterprise

This study evaluated the technology acceptance level of the proposed collaboration gamification framework based on the cultural principle "Silaturrahmi" and intelligent system (ISb-GM). This study uses the TAM (Technology Acceptance Model) to evaluate user acceptance of the ISb-GM framework by adding four attributes from the collaboration's parameter. 36 hypotheses are formulated with a sample of 293 respondents. Proving the hypothesis resulted in 29 hypotheses being accepted, while seven were rejected. These results suggest that the proposed framework is generally accepted. In comparison, the rejected hypothesis informs that technology needs to be applied more extended and continuously so that users can learn more about it and feel the benefits. This research has two implications. Firstly, SME developers can consider and implement this proposed framework in their communities to improve collaboration performance. Second, gamification technology developers can consider and improvise proposed gamification mechanics to develop collaboration mechanics for various fields.
Break-even point of the phase-flip error correcting code

Abstract In this theoretical study, we explore the use of quantum code-based memories to enhance the lifetime of qubits and exceed the break-even point, which is critical for the implementation of fault-tolerant quantum computing. Specifically, we investigate the quantum phase-flip repetition code as a quantum memory and theoretically demonstrate that it can preserve arbitrary quantum information longer than the lifetime of a single idle qubit in a dephasing-time-limited system, e.g. in semiconductor qubits. Our circuit-based analytical calculations show the efficiency of the phase-flip code as a quantum memory in the presence of relaxation, dephasing, and faulty quantum gates. Moreover, we identify the optimal repetition number of quantum error correction cycles required to reach the break-even point by considering the gate error probabilities of current platforms for quantum computing. Our results provide guidelines for developing quantum memories in semiconductor quantum devices.
Research on Video Pedestrian Tracking Based on the Combination of Optical Flow Method and Target Tracking Network

Target tracking is designed to predict the position of a given target in each video frame, and it has a wide range of applications in the fields of robot vision, video surveillance, and unmanned driving. This paper mainly studies the challenges of interference and target occlusion of similarities in target tracking, takes pedestrians as the main research target, combines the optical flow algorithm with the target tracking network, and improves the optical flow algorithm and the target tracking model, thereby improving the robustness of the tracking algorithm. Extensive experiments and comparative tests show the effectiveness of our method.
The Influence of Digital Marketing on Consumer Behavior in the Context of Online Shopping in the Klang Valley, Malaysia

The contemporary business landscape has undergone significant transformations, resulting in a highly competitive atmosphere. The onset of the second wave of digitization and technological innovation occurred in the early 2010s, and the COVID-19 pandemic further amplified its momentum. The second wave pertains to digitalizing the industrial sector, encompassing several aspects such as digital manufacturing, data-driven healthcare, intelligent buildings, and smart cities (World Economic Forum, 2021). Digital marketing has emerged as a highly prevalent marketing strategy organizations employ, mainly through social media platforms and email marketing techniques. Social media and email marketing are potent strategies for cultivating consumer engagement. This engagement plays a crucial role in influencing the frequency of customers' intent to make purchases. Furthermore, these marketing methods are characterized by their user-friendly nature and resilience. Digital marketing technologies provide organizations with a convenient means to effectively promote their products and foster customer relationships at a reduced cost, resulting in heightened sales volume. This study investigates the relationship between digital marketing, consumer engagement, and purchase intention within the Klang Valley region. The primary objective is to optimize the utilization of the study findings by obtaining theoretical validation of the relationship between the variables and selecting the most suitable implications that contribute to identifying the influence of digital marketing on consumer engagement and purchase intention.
Off-axis, slit based MTF measurements at PTB

This article presents off-axis modulation transfer function (MTF) measurements in the image field covered by the azimuthal angle &theta; = &plusmn;20 deg and a defocus range of &Delta;z ∈ [−40 &mu;m, +40 &mu;m] with the slit based MTF measurement setup at PTB. The experimentally determined standard deviation of a set of N = 10 MTF repetition measurements under a certain set of repeatability conditions is employed to estimate the standard uncertainty uexp with respect to the repeatability of measurements in the setup. The importance of measurement repeatability and measurement reproducibility in the assessment of measurement accuracy and measurement uncertainty for interlaboratory comparisons is discussed.
Visual navigation for mobile robots based on deep reinforcement learning

Since visual navigation algorithms based on deep reinforcement learning (DRL) suffer from insufficient extraction of spatio-temporal features of images and poor target point-oriented exploration, we propose a new visual navigation framework. The input of the framework consists of three components: the depth images generated by the depth camera at successive moments, the current state of the mobile robot, and the relative position of the target point, which outputs the control commands of the mobile robot in an end-to-end manner through DRL. In addition, we design a feature extractor based on convolutional long short-term memory network (ConvLSTM), which not only captures the correlation between the depth images and target point information at consecutive moments, but also better obtains the key spatial layout and corresponding temporal features from the time-series images, thus reducing the exploration time. The designed network is trained in an unknown simulation environment and random Gaussian noise is added to the depth images to enhance the generalization ability of the network. The results show the algorithm achieves better results in terms of learning rate, convergence and navigation success rate, and also shows good generalization ability across scenes.
A New Island Genetic Algorithm for Path Planning Problems <sup>*</sup>

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.
Retracted: Evaluation of College English Teaching Quality Based on Improved BT-SVM Algorithm

[This retracts the article DOI: 10.1155/2022/2974813.].
A Secure Community: Mitigating Ransomware Threats to Small Businesses

Abstract: In recent years, the increasing use of computers and smartphones has changed the way businesses operate. But along with these changes, there has been a rise in cyber threats, especially for small businesses. This research study delves into the world of cybersecurity for small businesses, looking at the challenges they face. We analysed data and information from the internet sources to understand what small businesses know about cyber threats, how they protect themselves, and how cyberattacks affect them. The study gives us a clear picture of the cybersecurity situation in these communities and shows why it is important to keep their digital assets safe. By sharing this information, we hope to help small businesses, make them stronger, and create a safer online world for everyone.
Threats of Adversarial Attacks on Deep Learning

Abstract: AI, accompanied with Deep Learning, is a growing technological innovation which has an extensible reach to various applications. The complex computational models i.e. Deep Neural Networks (DNNs) excel in natural language processing, image recognition, computer vision, and autonomous systems. Despite the merits, these intricate networks are vulnerable to adversarial attacks (such as black-box and white-box) which can be the primary threats to the AI-DL integration. For their mitigation, adversarial training, defense distillations and other defenses are introduced. This paper deals with the impediments posed by adversarial attacks on Deep Learning, and lists out the possible solutions to reduce their impacts.
Learn All About HTTP Testing: Book Update

More and more R packages access resources on the web, and play crucial roles in workflows.Examples from the rOpenSci suite of packages include rromeo, GSODR, qualtRics, rnassqs, and many, many others.Like for all other packages, appropriate unit testing can make them more robust.However, unit testing of these packages can bring special challenges: dependence of tests on a good internet connection, testing in the absence of authentication secrets
How to Use System Commands in your R Script or Package

Have you ever found a command-line tool that’s perfect for getting your job done, and wanted to use it from an R script or package?E.g. some sort of scientific software providing a specific functionality made available though a command-line interface (CLI)? In this post, we have a look at a few options for writing such CLI wrappers in R.In particular, we compare the base R functions system() and system2(), the sys package and the processx packag
Design of Styrofoam Cutting Machine Based on CNC 2 Axis Using Hot Wire

The development of the creative industry in the manufacture of decorations from styrofoam materials is increasing rapidly, the need for a 2-axis CNC-based automatic styrofoam cutting machine (Computer Numerical Control) using hot wire can facilitate the process of cutting styrofoam in large quantities and uniformly with machine drive on the X and Y axes. The purpose of this study is to design and manufacture a CNC based styrofoam cutting machine that can be programmed so that it can facilitate the cutting of styrofoam in large quantities and uniformly with movement on the X and Y axes. The method used is an experimental method in which the G-Code processing process is sent to the software which then produces 2 axis movements, namely on the X and Y axes. Then the styrofoam cutting process is continued using a hot wire whose temperature has been regulated using voltage and current which produces styrofoam cutting according to the size of the styrofoam used, which is 88 cm long, 42 cm high with a thickness of 2 cm and the accuracy level obtained for the X axis of 99.84% and the Y axis of 99.91%.
Evaluation on quantum illumination radar with quantum limited amplification

Based on Quantum illumination (QI) protocol, researcshers have developed prototypes of quantum radar and demonstrated its quantum enhancement. Nevertheless, there are still difficulties in the practical application for QI radar, especially the trade-off between the detection range and quantum enhancement, as well as the construction of the optimized receiver. Some studies have suggested that the potential solutions to these difficulties are to deploy the quantum limited amplifiers in QI radars, and have envisioned different amplification schemes. In this paper, we establish a universal and effective method to evaluate the signal-to-noise ratio of QI radar. It connects QI radar theory with classical radar signal processing theory, providing support for researchers to evaluate the performance of various QI radar schemes from a radar perspective. Based on this method, we prove that any quantum limited phase-insensitive amplification scheme will seriously weaken the quantum enhancement of QI radar. Furthermore, we also demonstrate that the QI radar with phase-sensitive amplified idler has no advantage over the optimal classical illumination. These results can help us avoid some unreasonable QI radar schemes. In addition, we believe that the proposed method can also be applied to explore other potential QI radar schemes and contribute to promoting the application development of QI radar.
Deep Convolution Modulation for Image Super-resolution

Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples. Yet intuitively, each image has its specific representation, and is expected to acquire an adaptive model. For this issue, we propose a novel convolution modulation (CoMo) mechanism to build image-specific deep networks, by exploiting the principal information of the feature to generate a modulation weight, and thereby adaptively modulating the kernel weights of convolution without any additional parameters, which outperforms the vanilla convolution and several existing attention mechanisms when embedding into the state-of-the-art architectures. To optimize the modulated convolutions in mini-batch training, we introduce an image-specific optimization (IsO) algorithm, which tackles the infeasibility of the conventional optimization algorithms on this issue. Furthermore, we investigate the effect of CoMo on state-of-the-art architectures and design a new CoMoNet architecture by employing the U-style residual learning and hourglass dense block learning, which is an appropriate architecture to utmost improve the effectiveness of CoMo theoretically. Extensive experiments on benchmarks show that the proposed methods achieve superior performances and higher flexibility against the state-of-the-art SISR and blind SR methods. The code is available at github.com/YuanfeiHuang/CoMoNet.