Reviewerly/exHarmony huggingface dataset:
Augmented Reality-based Oral Chinese Teaching System under Human-Computer Interaction

Mobile devices can make it easier for people to communicate and navigate information networks every time.Researching students' adoption of mobile learning is a crucial concern about broad technology possibilities.Courses of entrepreneurship are essential elements in the curriculum in the graduate program.Virtual realitybased user interfaces with hypertext systems (VR-UIHS) for collections of digital documents may be developed using a general technique presented in this study.Hence, this paper, Intelligent Mobile Assisted Entrepreneurship Learning System (IM-ELS), has been proposed to optimize the entrepreneurship education learning scheme and enhance students' structure of entrepreneurship expertise.Artificial intelligence has been used to track students' actions to enhance sustainable enterprise courses.The proposed method for maximizing the entrepreneurial education curriculum system is to improve student entrepreneurship structure and positively advance sustainable entrepreneurship education.Assumptions concerning meaningful learning in higher education and learning through hypertext are laid forth in this paper.In representing the Hypertext system graphically on information retrieval and structural modeling approaches.Associative networks are used to visualize the most important connections in the system.The user interface provides a consistent spatial paradigm that allows users to explore and browse the digital library's 3D virtual environment.The results show a strong learning outcome of 97.8%, enhanced interface usability of 95.7 %, estimation ratio of 98.4 %, the response of 96.1 %, an error rate of 17.6 %, a data transfer rate of 95.5 %, a performance rate of 91.8 %, and student happiness of 94.2 % all have a substantial impact on the implementation of mobile learning (m-learning) programs.
Geophysical Researches of Portals Will Allow to Discover Invisible Universes and Explore Them

The article proves that the version of the special theory of relativity (STR) presented in physics textbooks is incorrect. This is because in the early 20th century science lacked experimental knowledge required for the STR to be created, and the postulate (called the principle of light speed non-exceedance) that replaced the knowledge turned out to be incorrect and has been experimentally refuted in the 21st century. It is explained that tsunami and piano music would not exist, church bells would not ring and even swings would not swing on playgrounds, if the generally accepted version of the STR were true. Moreover, this version of the STR also implies that Ohm’s law as interpreted by Steinmetz used daily by millions of radio and electrical engineers in their practice does not exist, and therefore radio engineering and electrical engineering should not exist either. That is why, an alternative version of the STR has been created instead of the incorrect one. It follows from this that there is an invisible Multiverse whose universes are interconnected by numerous portals, including those located on Earth. And at least some of anomalous zones are entrances to portals. Geophysical exploration of portals is very necessary, as they will allow us to obtain new valuable knowledge about our Multiverse and confirm the correctness of the alternative version of STR.
DPI DCSK Modulation with BCJR Decoding

This paper proposes a novel [Formula: see text]-ary differential permutation index (DPI) differential chaos shift keying (DCSK) by using the Bahl–Cocke–Jeline–Raviv (BCJR) decoding (DPI-DCSK-BCJR) for chaos-based communications. By exploiting the characteristics of differential modulation and quasi-orthogonality of chaotic signals, the proposed DPI-DCSK-BCJR can provide two more calculated energies to decide the transmitted bits. Theoretical analysis of bit-error-rate (BER) is carried out over multipath Rayleigh fading channel and then validated via simulations. The proposed scheme is demonstrated to outperform the conventional DPI-DCSK by 2[Formula: see text]dB for different [Formula: see text]. This performance gain is especially increased over high-delay channels. Furthermore, the advantage of the proposed frequency-modulated DPI-DCSK-BCJR is verified over practical ultra-wideband (UWB) wireless channels. In addition, we investigate the proposed DPI-DCSK-BCJR for more generalized multiuser communications. The simulation result shows that DPI-DCSK-BCJR can achieve performance gains up to 5[Formula: see text]dB as compared to the conventional PI-DCSK and DPI-DCSK for both two and three user systems. Consequently, the proposed DPI-DCSK-BCJR can be considered as a low-cost alternative for low-complexity and high-rate chaotic-based wireless communication.
Introduction Aquatic Plants With Website Media In Purwodadi Botanical Garden

The Purwodadi Botanical Garden is one of the ex situ plant protection areas in Indonesia. One of the plant collections at Purwodadi Botanical Garden is a collection of aquatic plants. Aquatic plants are known to have many uses, as medicinal ingredients, feed ingredients, bioindicators and phytoremediators, and ornamentals in general. However, people's understanding of aquatic plants is still lacking. This research aims to develop digital media for Introduction to Aquatic Plants (PenTA) in Purwodadi Botanical Gardens (PBG) in the form of a website to facilitate public access. Multiple media are available on the site including videos, brochures, booklets and modules. In addition, there are also 32 ponds in the PBG with information about the plant species in these ponds. This media website aims to make it easier for the public to find out about aquatic plant
Twitter Sentiment Analysis

Twitter Sentiment Analysis is the process of extracting opinions, sentiments, and emotions expressed in tweets. This analysis can be used to determine the overall public opinion on a particular topic or to gauge the sentiment of customers towards a product or service. We use Textblob to classify tweets as positive, negative or neutral based on their content. We start by collecting a dataset of tweets using the Twitter API and manually labelling them as positive, negative or neutral. We are doing preprocess the text by removing stop words, punctuation, and special characters, and convert the text into a bag-of-words representation. We then use Textblob to train a model on the preprocessed data and test it on a separate set of tweets. Keyword: Textblob, twitterdataset, Twitter sentiment analysis
A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research

Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-IV version. Besides, the need to use multicenter datasets further highlights the challenge of EHR data extraction. Therefore, we developed an extraction pipeline that works on both MIMIC-IV and eICU Collaborative Research Database and allows for model cross validation using these 2 databases. Under the default choices, the pipeline extracted 38,766 and 126,448 ICU records for MIMIC-IV and eICU, respectively. Using the extracted time-dependent variables, we compared the Area Under the Curve (AUC) performance with ​​prior works on clinically relevant tasks such as in-hospital mortality prediction. METRE achieved comparable performance with AUC 0.723–0.888 across all tasks with MIMIC-IV. Additionally, when we evaluated the model directly on MIMIC-IV data using a model trained on eICU, we observed that the AUC change can be as small as +0.019 or −0.015. Our open-source pipeline transforms MIMIC-IV and eICU into structured data frames and allows researchers to perform model training and testing using data collected from different institutions, which is of critical importance for model deployment under clinical contexts. The code used to extract the data and perform training is available here: https://github.com/weiliao97/METRE.
LEARNING TASK AS THE HEART OF LEARNING ACTIVITY

A learning task introduces a situation that encourages children to search for general ways to solve a wide class of similar practical problems. The formulation and solution of the learning task involves the following actions of educators before they meet their students: 1) to perform a logical analysis of the concepts to be learned highlighting the “germ cells” - key general concepts from which the entire system of concepts describing the subject of study is derived; 2) to find a system of objects and actions with these objects, which would provide the child with a prospect to notice the essential properties of reality depicted by the concept; 3) to construct signs or symbols as the means of acting with this concept in the absence of real objects; 4) to design a situation that can lead children to posing a question, the answer to which will resolve into a new concept. When meeting with students, the teacher seeks after: 1) encouraging children for independent search for ways to solve a new problem, instead of presenting a ready-made pattern for solving such problems; 2) rousing, and then maintaining, children's initiatives aimed at the issue of their research: questions, assumptions, observations; 3) creating conditions for children’s motivation and ability to interact with their classmates so that they understand and develop each other’s suggestions about the ways to solve a new problem. When the teacher succeeds with learning tasks in the elementary school, a learning community emerges, capable and inclined to set and solve new learning tasks on their own initiative. Then later, in the middle school, most students who grew in such a community develop the learning to learn ability and the sustained drive for lifelong learning.
An Exploratory Framework for Intelligent Labelling of Fault Datasets

Software fault prediction (SFP) has become a pivotal aspect in realm of software quality. Nevertheless, discipline of software quality suffers the starvation of fault datasets. Most of the research endeavors are focused on type of dataset, its granularity, metrics usedand metrics extractors. However, spo-radic attention has been exerted on developmentof fault datasets and their associated challenges. There are very few publicly available datasets limiting the possibilities of comprehensive experiments on way to improvising the quality of software. Current research targets to address the challenges pertinent to fault dataset collection and developmentif one is not available publicly. It also considers dynamic identification of available resources such as public dataset, open-source software archieves, metrics parsers and intelligent labeling techniques. A framework for datasetcollection and development process has been furnished alongwith evaluation procedure for the identified resources.
Improved YOLOv5s algorithm for small item detection of wheelhouse

With the development of deep learning, object detection has achieved rapid development in recent years, and is widely used in real-life scenarios such as face detection and automatic driving. In the field of ship navigation safety, it is necessary to identify whether some specific items appear in the wheelhouse to help determine whether there is a threat to drive safety. These items are usually small in size and require higher detection efficiency. To address this problem, this paper proposes a ship-specific item detection method that improves the YOLOv5s algorithm. By introducing the convolution attention mechanism module CBAM, the feature extraction ability of the network, the detection capability of small targets, and the detection accuracy are improved. The experimental results show that after the introduction of the attention mechanism, the precision rate of YOLOv5s on ship-specific items is 85.6%, the recall rate is 85.2%, and the average accuracy is 90.2%, which can complete the detection task of specific items of wheelhouse small targets
Optimisation of circuit for quantum automata

he paper considers the implementation of a finite quantum automaton, recognizing the unary language MOD_p using the qiskit library and the language python programming. We also present an optimization of the quantum schemes for implementing the distributed controlled algorithm rotation used in the implementation of the aforementioned quantum machine.
Contents list

Permissions Request permissions Contents list J. Anal. At. Spectrom., 2023, 38, 779 DOI: 10.1039/D3JA90016A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.
Social media as an English learning platform during the pandemic

Online learning is not always about using learning platforms explicitly designed for learning. This study aimed to reveal the students' perception of using one social media as the primary learning platform in an extensive reading subject as one of the language learning strategies. This case study used open-ended questionnaires and semi-structured interviews as the instruments to gather the data. There were 64 students (52 females and 12 males) from three classes and a lecturer, chosen using purposive sampling as the participants. The findings showed that the use of Facebook as one of the social media platforms for online learning was considerably effective and satisfying, and the students' questionnaire results and lecturer interviews revealed that Facebook has easy-to-use features and could be used as communication media. This study suggests that educators were expected to start broadening the use of their social media applications so those can also be used as platforms for their teaching activities in the classroom.
AN ANALYSIS OF SPEECH ACT DURING SPEAKING CLASS ACTIVITY (TEACHER – STUDENTS TALKS)

This research focuses on speech acts as a significant role in teaching and learning process. In line with this topic, the aimed of this research attempts to find out the types of speech act mostly used by teachers’ and students’ English program. This study used descriptive qualitative method. The data which were used the utterances performed by the teacher and students talk in speaking class. Those speech acts are supported by theories of Searle (1977) who find out the use of speech act. From the theory, there are 5 classifications of illocutionary speech act; representative, directive, expressive, commissive, and declarative. The findings show that the classifications of speech acts mostly used by teacher is directive and representative is mostly used by the students. From the findings, teacher’s directive has some involvement toward students’ behavior during teaching and learning process. Besides, the used of representative by students has enhances their speaking ability. This study concludes that when teacher and students used the speech acts, it helps them comprehend the message in every utterance and easy to understand when communication. Hopefully, this study will help develop good classroom communication between teachers and students.
Implementing QR codes in academia to improve sample tracking, data accessibility, and traceability in multicampus interdisciplinary collaborations

The growing number of multicampus interdisciplinary projects in academic institutions expedites a necessity for tracking systems that provide instantly accessible data associated with devices, samples, and experimental results to all collaborators involved. This need has become particularly salient with the COVID pandemic when consequent travel restrictions have hampered in person meetings and laboratory visits. Minimizing post-pandemic travel can also help reduce carbon footprint of research activities. Here we developed a Quick Response (QR) code tracking system that integrates project management tools for seamless communication and tracking of materials and devices between multicampus collaborators: one school of medicine, two engineering laboratories, three manufacturing cleanroom sites, and three research laboratories. Here we aimed to use this system to track the design, fabrication, and quality control of bioelectronic devices, in vitro experimental results, and in vivo testing. Incorporating the tracking system into our project helped our multicampus teams accomplish milestones on a tight timeline via improved data traceability, manufacturing efficiency, and shared experimental results. This tracking system is particularly useful to track device issues and ensure engineering device consistency when working with expensive biological samples in vitro and animals in vivo to reduce waste of biological and animal resources associated with device failure.
Session-based concurrency in Maude: Executable semantics and type checking

Session types are a well-established approach to communication correctness in message-passing processes. Widely studied from a process calculi perspective, here we pursue an unexplored strand and investigate the use of the Maude system for implementing session-typed process languages and reasoning about session-typed process specifications. We present four technical contributions. First, we develop and implement in Maude an executable specification of the operational semantics of a session-typed π-calculus by Vasconcelos. Second, we also develop an executable specification of its associated algorithmic type checking, and describe how both specifications can be integrated. Third, we show that our executable specification can be coupled with reachability and model checking tools in Maude to detect well-typed but deadlocked processes. Finally, we demonstrate the robustness of our approach by adapting it to a higher-order session π-calculus, in which exchanged values include names but also abstractions (functions from names to processes). All in all, our contributions define a promising new approach to the (semi)automated analysis of communication correctness in message-passing concurrency.
The The boosting academic literacy for young learners’ reading comprehension and character building

Reading habit is important to be introduced to young learners. Through academic literacy, students not only comprehend social and science texts but also enhance their character. To realize the literacy program in these lessons, a team-game tournament is used that consists of a presentation, group discussion, reading tournament, and the award. The study uses a qualitative research method that applies a semi-structured observation guide and interviews sixth-grade students in one primary school in Indonesia. The observation indicates teaching techniques for improving reading comprehension, and the interview shows the impacts of academic literacy in the team-game tournament (TGT) on students’ knowledge. The findings show that academic literacy improves understanding of social and science texts, and moral values. The study recommends that team-game tournament provides practice not only in reading but also in writing and speaking skills.
A Proposed Framework for the Development of the Internal Control System According to the COSO Model: An Applied Study

The research aims to develop the internal control system in Gaza&amp;#39;s municipalities in accordance with the proposed COSO model to achieve the goal for which it was found. The research used the analytical descriptive curriculum to prepare the applied aspect of the study, where the two researchers used data collected by the study tool, a questionnaire that is specifically designed for that purpose. The research followed a thorough survey methodology to apply the study instrument to all 122 Ministry of Local Government employees who operate in the various control units in the Gaza Strip municipalities. The results of the research indicated the positive impact of all components of the COSO model (control environment, control activities, risk assessment, information and communication systems, and monitoring and follow-up) on the development of the internal control system in the municipalities of the Gaza Strip. The research recommends that you should have to rely on a control manual based upon the updated COSO model and standards to be commensurate with the work and activity of local bodies. Furthermore, the establishment of a specialized department for fundraising to oversee project finance from donors and the activation of an awareness and control guidance committee within the municipal sector to raise awareness of the monitoring role.
Chuvash department of the Eastern Pedagogical Institute (by the manuscript fund of N.V. Nikolsky)

The article analyzes the activities of the Chuvash department of the Eastern Pedagogical Institute in 1926–1930. The sources of the article were the materials of the Manuscript Fund of Professor N.V. Nikolsky in the Scientific Archive of the Chuvash State Institute of Humanities. There are reports, protocols, reviews, correspondence and other documents that reveal the role of N.I. Nikolsky, N.A. Ashmarin, G.I. Komissarov, V.E. Egorov in the development of higher education in the Chuvash ASSR. The research activity in the Chuvash department was particularly noted.
Plug &amp; play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.
Corrigendum: Facial expression recognition method based on PSA—YOLO network

[This corrects the article DOI: 10.3389/fnbot.2022.1057983.].
IOT Based Smart Cart System

Object detection is a widely studied area of computer vision that has seen significant improvements over the years. However, most of the existing methods rely on high-end GPUs or specialized hardware, making them impractical for real-time detection on mobile devices. In this paper, we present a novel approach to object detection using a mobile camera. Our method utilizes a light weight convolutional neural network architecture and employs transfer learning to achieve high accuracy while keeping computational complexity low. We evaluate our method on a dataset of common objects and achieve a mean average precision (mAP) of 0.85. We also demonstrate the practicality of our approach by implementing it on a mobile device and achieving real-time object detection.