Reviewerly/exHarmony huggingface dataset:
Students’ Attitudes toward the Use of Podcast as Digital Material in EFL Classroom

Some previous studies mentioned that students showed a negative attitude toward EFL learning because they felt bored with printed materials like textbooks. Hence, this study was conducted to implement digital material in EFL classrooms and observe students' attitudes and difficulties in experiencing digital material. Moreover, 30 students from eleven grades used podcasts as digital material to learn exposition paragraphs. This study used questionnaires and interviews as the data collection to describe students' cognitive, affective, and behavioral attitudes as well as the difficulties in using the digital material. This study revealed that students showed a positive cognitive attitude in understanding and comprehending digital material. While the use of podcasts could assist them in producing the exposition text, it also triggers their critical thinking. Besides, students also felt interested and happy because they could choose their podcast as the learning material, and it reflected a positive affective attitude. The students' activeness in classroom interaction was improved after using the digital material, and it showed their positive behavioral attitude.Furthermore, students also faced some difficulties, such as acquired the limitation of an internet connection to access the podcast and being confused about choosing a reliable podcast. In addition, this study showed that podcasts reflect the function of supplementary and complementary digital learning material in EFL classrooms. Finally, this study followed by suggestions for the English teacher to improve the teaching and learning process using digital material.&#x0D; Keywords: Attitudes, Digital Material, Exposition text, Podcast
An Overview of Robust Video Watermarking Techniques

Copyright hacking and piracy have increased as the Internet has grown in popularity and access to multimedia material has increased. Security, property protection, and authentication have all been achieved via watermarking techniques. This paper presents a summary of some recent efforts on video watermarking techniques, with an emphasis on studies from 2018 to 2022, as well as the various approaches, achievements, and attacks utilized as testing measures against these watermarking systems. According to the findings of this study, frequency-domain watermarking techniques are more popular and reliable than spatial domain watermarking approaches. Hybrid DCT and DWT are the two most used techniques and achieve good results in the field of video watermarking, and the system is more robust against attacks. PSNR was taken into consideration as a comparison metric in various recent articles in terms of robustness in the video watermarking field.
Auto-Markup BenchMark: Towards an Industry-standard Benchmark for Evaluating Automatic Document Markup

Recent large language models (LLMs) such as GPT, Bard and Claude have achieved impressive progress in comprehending and generating human language. We explore the potential for LLMs to automate markup of semi-structured texts into HTML, DITA, and other related languages. Automated markup could make it possible to move documents into XML at much lower cost. Currently, there is no standard benchmark for evaluating automatic markup systems. Establishing a benchmark for the understanding and generation of structured documents and markup languages can drive innovation, standardize evaluations, identify algorithm strengths and weaknesses, clarify the State of the Art and foster interdisciplinary collaborations. This paper introduces an early version of a benchmark for this purpose.
Auto-Processing Of Filtered Rayleigh Scattering Images Including Mie And Background Scattering Contributions

Filtered Rayleigh scattering (FRS) is a non-intrusive, optical-based technique that allows for simultaneous, time-averaged, measurements of three-component velocity, static temperature, and static pressure. The method of post-processing the raw images to get these variables is a non-trivial task. The post-processing scheme starts with building a model to generate simulated spectra given known velocity, temperature, and pressure values which can be iterated upon. The model also includes Mie scattering and background scattering contributions that must be taken into account. This iteration scheme allows for the simulated spectra to be matched to the experimental spectra to back out the desired flow variables. This iteration process can be lengthy and so it is important to make spectrum generation/iteration as fast as possible. This is done through the use of support vector spectrum approximation (SVSA), which is a machine learning algorithm, as well as a multivariable minimum error solver based on the least-squares fit between the simulated data and the experimental data. To prove the methodology, simulated results were first compared at different signal-to-noise ratios to determine the expected uncertainty at each SNR level. It was shown that to achieve an error of less than 1\% in all variables (velocity, static temperature, Mie intensity ratio, and background intensity ratio), the SNR must be near 40dB. This kind of SNR level is generally not expected in an experiment. Therefore, a representative experiment was conducted which included Mie and background scattering contributions. It was shown that manually processed data was capable of achieving velocity results that were within 2\% of the probe data. No other variables were achieved. The auto-processing scheme was able to achieve an error of approximately 2.5\% in velocity when compared to probe data. It was also capable of providing static temperature, Mie intensity ratio, and background intensity ratio values. The manual iteration results took several days to achieve while the auto-processing took about an hour. It is clear that this methodology is capable of automatically processing images from experiments with Mie and background scattering contributions while saving time.
An Analysis Of The Performance Of Autonomous Navigation On An Ardupilot-Controlled Rover

Monitoring forests is one of the strategies in the overall preventive strategy. Monitoring the forest can quickly and permanently manage how tree illnesses emerge, spread, and evolve. To help monitor forest fires, a robot platform that can operate independently and assist in data collection can be created. In this paper, the accuracy of the Ardupilot-controlled autonomous navigation system of the rover was examined. The metode are used is experimental study, the study consists of a comparison between the GPS rover log and the SITL simulation within the mission planner tool. The average accuracy achieved by altering the route's distance and shape is 94.58%. The lengthy path may be the source of the rover's inaccurate autonomous navigation. In this case, the turning angle problem has no real effect on how well and accurately the rover navigates on its own.
The Social Impact of VR Technology on Society: A Systematic Literature Review

Humans are social creatures who usually interact directly. But as technology develops, interactions also use the technology. One of the many popular technologies in the 4.0 era is VR technology. An example of this technology is the metaverse, a combination of the real and virtual worlds. This paper presents a research review to analyze the social impact of Virtual Reality: technology with metaverse. This paper uses a systematic literature review (SLR) as the method. The research was conducted based on the RQ that had been made. There are 18 related articles generated from the search, then reviewed. This study concludes that Social VR has three impacts: Social Presence Illusion, Virtual Embodiment, and the Proteus Effect. In addition, we also explain the ways to create a safe social VR environment, such as by playing with family and friends, educating the public about VR technology, and suggesting that the platform provides an age limit for playing in a VR environment.
Web Store Teh Krisan (erpekrisan) Rachmat Purnama Farm

The Rachmat Purnama Farm Company, a producer of chrysanthemum flowers located in Cianjur Regency, has made efforts to process chrysanthemum flowers into chrysanthemum tea. However, the marketing remains limited, as sales are conducted directly. Consequently, the company recognizes the need for an online web store to enhance its marketing reach. The purpose of this system is to provide customers with information about chrysanthemum tea and facilitate convenient purchase transactions. The waterfall model is used as the software development method, which covers requirements analysis, system design, program code development, program testing, and program implementation and maintenance. The features of the online web store include account management, customer management, product categorization, product listings, attributes, sales, payments, shipping, and transaction reports. The implementation has positively impacted the company’s sales of chrysanthemum tea products, as shown by the increasing number of online purchases. Moreover, the marketing area coverage has expanded to Jakarta and Kalimantan.
Desain Pengembangan E-Modul IPA Materi Kalor Berbasis Flipbook Maker Dalam Meningkatkan Motivasi Belajar Siswa Kelas V Di Sekolah Dasar

This study aims to produce a product development design for the Science E-Module Heat Material Based on Flipbook Maker to increase the learning motivation of fifth-grade students at 60 State Elementary Schools, East City, Gorontalo City. The method used in this study refers to the Hannafin &amp; Peck model. Hannafin &amp; Peck’s model is a product-oriented research model. This model means that the product produced in this research is prioritized. At the same time, the effect produced in this study is an e-module of heat material based on flipbook maker in improving learning motivation of fifth-grade students. The E-Module of heat material design based on Flipbook Maker, which is designed using the Flipbook Maker application, has very feasible criteria of 95%. Thus, the E-module (electronic module) of heat material science designed using the Flipbook Maker application is included in the possible standards, can be used in learning, and is believed to generate student motivation in education, train students to think high-level and master technology.
Basic Technologies to Build a Complete Metaverse

Since the development of computers and screens capable of displaying a wide range of colors, people are starting to look at the bigger picture, such as 3D projection, online society, virtual reality, and so on. Nowadays, more and more technology companies are entering the field of virtual reality, they call the ideal project a metaverse. This paper will introduce the basic concepts and technologies of the metaverse, from the fundamental construction to different areas connections and a higher demand for services with advantages. So that there should have a comprehensive impression for people to understand how far from the final goal on the metaverse, how about the feasibility nowadays and development methods in the future. Although nowadays it still seems far from the ideal opinion, under the situation that many huge companies are making investment on it, we could presume the potentials of metaverse through its basics and have a better understanding of its benefits and limitations. Overall, the metaverse will change peoples social lives a lot, so its important to have a detailed understanding of this.
Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success

Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to workforce fluctuations.The past few years have seen increased interest in automating such repeated tasks but mostly in controlled settings.Tasks such as picking objects from unstructured, cluttered piles have only recently become robust enough for large-scale deployment with minimal human intervention.This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which utilizes a pick success predictor trained on real production data.Specifically, the system was trained on over 394K picks.It is used for singulating up to 5 million packages per day and has manipulated over 200 million packages during this paper's evaluation period.The developed learned pick quality measure ranks various pick alternatives in real-time and prioritizes the most promising ones for execution.The pick success predictor aims to estimate from prior experience the success probability of a desired pick by the deployed industrial robotic arms in cluttered scenes containing deformable and rigid objects with partially known properties.It is a shallow machine learning model, which allows us to evaluate which features are most important for the prediction.An online pick ranker leverages the learned success predictor to prioritize the most promising picks for the robotic arm, which are then assessed for collision avoidance.This learned ranking process is demonstrated to overcome the limitations and outperform the performance of manually engineered and heuristic alternatives.To the best of the authors' knowledge, this paper presents the first large-scale deployment of learned pick quality estimation methods in a real production system.
Development of Interactive E-Modules Based on Kvisoft Flipbook with Discovery Learning Models on Arthropod Material to Improve Student Learning Outcomes

Education is a learning activity that determines the success of achieving student learning outcomes in class. So, to achieve learning goals by increasing student learning outcomes, it is necessary to have a good and correct learning process with the help of facilities, good learning methods.This study aims to determine the effect of an interactive e-module based on kvisoft flipbook with the discovery learning model on arthropod material on student learning outcomes. This research uses development research (R&amp;D) with the ADDIE model which consists of five stages, namely Analysis, Design, Development, Implementation, and Evaluation. This research reached the evaluation development stage. Phase testing for e-module development in the form of validation (media expert, material expert and pedagogic expert) and testpracticality and student response. The results of the validation analysis are in the very valid category. Practicality is in the very practical category used in learning, and student responses are in the very good category and are well received by students in learning.
End-to-end neural speaker diarization with an iterative adaptive attractor estimation

End-to-end neural diarization (EEND) which has the capability to directly output speaker diarization results and handle overlapping speech has attracted more and more attention due to its promising performance. Although existing EEND-based methods often outperform clustering-based methods, they cannot generalize well to unseen test sets because fixed attractors are often utilized to estimate speech activities of each speaker. An iterative adaptive attractor estimation (IAAE) network was proposed to refine diarization results, in which the self-attentive EEND (SA-EEND) was implemented to initialize diarization results and frame-wise embeddings. There are two main parts in the proposed IAAE network: an attention-based pooling was designed to obtain a rough estimation of the attractors based on the diarization results of the previous iteration, and an adaptive attractor was then calculated by using transformer decoder blocks. A unified training framework was proposed to further improve the diarization performance, making the embeddings more discriminable based on the well separated attractors. We evaluated the proposed method on both the simulated mixtures and the real CALLHOME dataset using the diarization error rate (DER). Our proposed method provides relative reductions in DER by up to 44.8% on simulated 2-speaker mixtures and 23.6% on the CALLHOME dataset over the baseline SA-EEND at the 2nd iteration step. We also demonstrated that with an increasing number of refinement steps applied, the DER on the CALLHOME dataset could be further reduced to 7.36%, achieving the state-of-the-art diarization results when compared with other methods.
D-GATE: Decentralized Geolocation and Time Enforcement for Usage Control

In the context of cloud environments, data providers entrust their data to data consumers in order to allow further computing on their own IT infrastructure. Usage control measures allow the data provider to restrict the usage of its data even on the data consumer's system. Two of these restrictions can be the geographic location and time limitations. Current solutions that could be used to enforce such constraints can be easily manipulated. These include solutions based on the system time, organizational agreements, GPS-based techniques or simple delay measurements to derive the distance to known reference servers. With D-GATE, we propose a reliable solution that uses trusted execution environments and relies on a decentralized mesh of reference nodes, so-called GeoClients. Here, participants periodically measure the lowest network delay to each other to geolocate themselves. For data providers, it is thus possible to technically attest usage control with time and geolocation constraints without depending on centralized reference systems.
Setting Up and Implementing ArcGIS to Work with Maps and Geospatial Data with Python for Teaching Geoinformation Systems in Higher Education

Geoinformation systems, as teaching tools in education, are closely linked to interdisciplinary and natural sciences. Currently, the application of geoinformation systems (GIS) in the educational process holds great potential for higher education. This paper presents a pedagogical system for teaching and learning a GIS course, along with a wide range of information sources that educators can choose from. The pedagogical system of introducing special GIS courses is divided into content, organizational and methodological structures. The theoretical and practical foundations of the introduction of special GIS courses are also considered. Additionally, the paper provides information about practical works setting up and implementing ArcGIS to work with maps and geospatial data with Python. The pedagogical system described in this paper was employed in the educational process of the L.N. Gumilyov Eurasian National University (Astana, Kazakhstan) and K. Zhubanov Aktobe Regional University (Aktobe, Kazakhstan). This experimental research examines how perceptions of geocapabilities evolved among 119 higher education students as they acquired technical skills in web mapping by investigating real-world issues with ArcGIS.
Use of Big Data Technology for Network Classroom Teaching Quality Management

Quality management of network classroom teaching has always been an urgent problem to be solved. Big data technology handles massive amounts of data and provides new quality management methods and means for network classroom teaching. However, data integration and fusion is a complex task and existing methods may not be able to deal with data fragmentation effectively, because data is often distributed across different systems and platforms in the network teaching environment. Therefore, this research aimed to study the quality management of network classroom teaching based on big data technology. This study provided a framework diagram of teaching quality evaluation criteria and factors affecting the teaching quality in the big data environment, explained complex relationships and effects among the factors, and described teaching quality prediction problems. The dimensionality reduction method of Least Absolute Shrinkage and Selection Operator (LASSO) was used for comprehensive status data integration of factors affecting teaching quality. An unequal-interval grey Riccati-Bernoulli model was constructed to study the internal relationships between various variable factors and network classroom teaching quality. Then the execution process of the prediction model, detailed modeling steps and teaching quality management steps were provided. The experimental results verified that the constructed model was effective.
A Secure Method of Communication in Conventional Cryptography using Quantum Key Distribution

Security knowledge is one of the foremost challenges in the present day. When the topic is about Information security, the concept of cryptography comes into the picture. Every day, people and organizations use cryptography to maintain the confidentiality of their communications and data as well as to preserve their privacy. Today, one of the most successful methods used by businesses to protect their storage systems, whether at rest or in transit, is cryptography. Yet, cryptography is an effective technique to secure the data, the modern technology can break the cryptographic techniques. But some data encryption algorithms are several times stronger than today's conventional cryptography and can be constructed using quantum computing. They are "Quantum Cryptographic Algorithms ". Quantum cryptography uses the rules of quantum physics instead of classical encryption, which is based on mathematics, to protect and transmit data in a way that cannot be intercepted. Quantum key distribution is the greatest illustration of quantum cryptography and offers a safe solution to the key exchange issue. The proposed work deals with quantum cryptography and mainly focuses on how the quantum cryptographic algorithm is more secure than traditional cryptography.
Application and Research of VR in Education

The development of computer hardware and computer graphics had led to a new kind of new form of visualization which is called Virtual Reality (VR) technology. People could play games and communicate with others, but most importantly, children are more likely to learn things through VR. This essay mainly examines what are the ways of how VR educational games, art educations, and political education, contribute to help students to learn while they are playing VR and what are the differences between VR and other forms of education. The research summarizes the advantages and disadvantages of VR in educational games, and analyses its application in different art education scenarios. Some problems of VR in political education are discussed. This paper may provide readers with new ideas and thoughts on the potential of VR in the future that are beneficial to the entire society. At the end of the paper, the paper summarizes the full text and looks forward to the future.
Bosonic Gaussian channel and Gaussian witness entanglement criterion of continuous variables

We use quantum entanglement witnesses derived from Gaussian operators to study the separable criteria of continuous variable states. For bipartite system, we transform the validity of a Gaussian witness to a bosonic Gaussian channel problem. It follows that the maximal means of two-mode and some four-mode Gaussian operators over product pure states are achieved by vacuum (or coherent states and squeezed states) according to the properties of bosonic Gaussian channels. We demonstrate necessary and sufficient criteria of separability for two-mode and some four-mode Gaussian quantum states. We also propose multipartite Gaussian witnesses based on numeric evidence. The entanglement and multipartite entanglement detecting powers of the Gaussian witness criteria are illustrated by the mixture of two Gaussian states. We apply the Gaussian witness criterion to a Gaussian Fock state, which is prepared by passing a product Fock state through bosonic Gaussian channel. We show that the necessary criterion of separability for a Gaussian Fock state does not rely on the initial product Fock state, it relies on the channel.
Special Issue “Algorithms for Feature Selection”

This Special Issue of the open access journal Algorithms is dedicated to showcasing cutting-edge research in algorithms for feature selection [...]