Reviewerly/exHarmony huggingface dataset:
Communication model of movie story scene

Each literary genre is a way of organizing works, a type of life representation, and a particular type of artistic communication. One of the ways to approach the film script as a literary genre is to recognize its communicative characteristics. The communication model of the movie script is closely associated with and imprinted with the film production process. Drama texts are placed in a hierarchical communication model, with diverse communication roles, where the screenwriter has a special role and fate. The screenplay for a feature film has a dual identity, being both a text - a message - in the first communication activity (between the elements of the film production process), and plays the role of a transmitter in communication activities. second (between the factors that produce the film, the film, and the viewer). It is both encoded and transcoded (encodes the writer's message; transcodes language into code that synthesizes images and sounds). The dramatic text itself, when functioning in that communication activity, has different characteristics from the texts of other literary genres, that is, it is expressive and generative.
SMART WASTE MANAGEMENT BASED ON ARTIFICIAL INTELIGENCE PADA SAMPAH DI BANYUWANGI

The Banyuwangi government has problems with waste management, operational costs, and management facilities, the budget spent by the goverment is around 7 billion. Integrated Waste Disposal Sites and Landfills have 1 unit each. TPA should have 12 units and TPST 24 units based on Banyuwangi Regional Regulation. Based on the background, the researcher conducted a study on two systems in the management of waste and waste in Banyuwangi. First, Smart Waste Management (SWM) is based on Artificial Intelligence (AI). SWM integrates the Internet of Things (IoT) and AI for a waste monitoring platform in each sub-district. The algorithm consists of identification, automation, display, and Arduino Uno communication. The tools and materials used in this research include a shredderArduino Uno, GSM Module, etc, and several other electrical devices that support the operation of the system. Some of the support obtained from the local government and the environmental agency produced maximum results, where the smart technology system for waste and waste management designed by the UNIBA research team was maximized. Monitoring provides very accurate information about garbage and waste in Banyuwangi. With the monitoring system, the performance of waste officers will be maximized and efficient. In addition, the waste monitoring platform can be viewed on a smartphone. Based on the results of the above research, it can be concluded that waste in the Banyuwangi is only part of the area that uses Smart Waste and was successfully tested simultaneously. SWM provides information about garbage and waste in Banyuwangi very accurately.
Effectiveness and Improvement Plan of Competency-Based Major Curriculums Development and accreditation: Case of H University; Focusing on the professor’s experience

Objectives This study was conducted with the purpose of deriving the effect and improvement(&amp;accreditation) plan of H university's competency-based major curriculum development. Methods Qualitative exploration through individual interviews and quantitative collection through questionnaires were conducted for professors in the major curriculum of H University. Nine professors with experience in competency-based major curriculum development (&amp;accreditation) participated in individual interviews, and the collected qualitative data were analyzed in frequency according to the Consensual Qualitative Research-Modified (CQR-M) procedure by Hill (2016). A questionnaire was organized based on the results of qualitative analysis, and a survey was conducted on all professors of the major curriculum to collect opinions from H University education members. A total of 29 people responded to the questionnaire based on a 5-point Likert scale, and it was confirmed that there were no insincere responses, so the responses were analyzed based on the responses of 29 people. The following is a summary of the key contents derived based on the results of the qualitative data analysis and the comprehensive analysis of the quantitative data. Results First, as the main point of the development effect of the competency-based major curriculum, the core contents were derived by categorizing it into ‘systematization and improvement of the curriculum’ and ‘effects in terms of educational members’. Second, key contents were proposed in ‘content aspect of development’ and ‘process aspect of development’ as major improvements in the development of competency-based major curriculum. Third, the effect of the major curriculum accreditaion system can be said to be ‘internalization of the contents and methods of the curriculum centered on educational consumers’ and the opinion on ‘the need for accreditaion incentives’ was the highest. Fourth, it was interpreted that the most important improvement in major curriculum accreditation system needs to be reviewed as a accreditation index considering the educational direction of H University. Conclusions Based on the results of this study, the effective aspects of H University's competency-based major curriculum development and accreditation process were examined, and the future improvement and implications of the competency-based major curriculum development and accreditation system for continuous educational quality were discussed.
Focus group evaluation of an elective course design

This research led to determining the relevance of the curriculum designed by our Professional Training School for Teachers in Puebla, Mexico, regarding the dimensions of content and curricular order, based on the experience of students and teachers. The methodology has a qualitative approach; the technique was focus groups, and the instrument was a script of open questions that considered the internal evaluation of the process. It is in our best interest to strengthen the design and implementation of the "Inclusion, Art, Play, and Sport" course as part of a series of community-proposed optional courses. In the experience of students and teachers, the group met the general purpose. The feedback received through the focus groups cut in greater depth for students to identify the collective achievements of all the participants: students, teachers, and curriculum designers. Throughout the study, we identified aspects for improvement, such as rethinking some activities and learning evidence, reducing the amount of information to meet, and establishing a better relationship between the topics and the teaching practices, particularly those related to strategies for inclusion in art and sports.
A New Approach for Optimizing the Extraction of Association Rules

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.
Dries Tack - Adjacent Spaces. Dries Tack (clarinets). Orlando Records, or0046.

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CAS names its 2023 Future Leaders

Thirty-five early-career scientists make up the CAS Future Leaders class of 2023. The scientists will attend a program at CAS headquarters, in Columbus, Ohio, Aug. 7–12. The program, organized by CAS, a division of the American Chemical Society, provides PhD students and postdoctoral researchers with opportunities to network with peer scientists from around the world, be inspired by industry thought leaders, and participate in science leadership training. The participants will also attend ACS Fall 2023, in San Francisco, Aug. 13–17. The 2023 CAS Future Leaders are Justin L. Andrews, Massachusetts Institute of Technology; Sivani Baskaran, Norwegian Geotechnical Institute; Isaiah Borne, University of Liverpool; Andryj Borys, University of Bern; Kimberly Carter-Fenk, Stanford University; Alison S. Chang, University of Oregon; Ty Christoff-Tempesta, University of Delaware; Eleonora Comeo, University of Nottingham; Roxana Coreas, University of California, Berkeley; Cintya Dharmayanti, University of South Australia; Katharina Ehrmann, TU Wien; Ana C. Garcia Alvarez, University of
Utilizing Multi Stage Attestation for Secure Cloud Data Retrieval

Abstract: The Advancement in data storage has taken a major leap. Due to Current trending technologies that have been taken place many companies find it convenient to store the data in Cloud. Cloud Computing refers to the storage, networking, operations, etc... in a single place with a pay as you use pricing. Due to these kind of numerous advantages companies use cloud to store the data. Now the problem with the cloud is there is no specific Authentication measures that have been taken place while storing and retrieving the data. So, we are proposing a Multi Stage Authentication(MSA) method to encrypt and decrypt the data with the help of a Image that acts a Secondary stage of Authentication. As there is a three-step process that goes on when the data is transferring the whole process becomes reluctant to any data leaks or data loss.
An auxiliary attention-based network for joint classification and localization of breast tumor on ultrasound images

Ultrasound imaging is an effective screening tool for early diagnosis of breast tumor to decrease the mortality rate. However, differentiation of tumor type based on ultrasound images remains challenging in the field of medical imaging due to the inherent noise and speckles. Thus, obtaining additional information for lesion localization could better support the decision-making by clinicians and improve diagnosis fidelity. Recently, multi-task learning (MTL) methods have been proposed for joint tumor classification and localization, where promising results were demonstrated. However, most MTL methods trained independent network branches for the two different tasks, which might cause conflicts in optimizing features due to their different purposes. In addition, these methods usually require fully-segmented datasets for model training, which poses a heavy burden in data annotation. To overcome these limitations, we propose a novel MTL framework for joint breast tumor classification and localization, motivated by the idea of attention mechanism and weakly-supervised learning strategy. Our method has three major advantages. First, an auxiliary lesion-aware network (LA-Net) with multiple attention modules for lesion localization was designed on top of a pre-defined classification network. In this way, the extracted features for classification were directly augmented by the region of interest (ROI) predicted by the LA-Net, alleviating the potential conflicts between the two tasks. Second, a sequential training strategy with a weakly-supervised learning scheme was employed to train the LA-Net and the classification network iteratively, which allows the model to be trained on the partially-segmented datasets and reduces the burden on data annotation. Third, the LA-Net and classification network design are modularized so that both architectures can be flexibly adjusted for various applications. Results from experiments performed on two breast ultrasound image datasets demonstrated the effectiveness of the proposed method.
SMART NEWS APP IMPLEMENTATION FOR COLLEGE

Users of this news app for Android smartphones may get the most recent news from our college.This application's primary goal is to gather college news and quickly and effectively communicate it to the students.Our research makes use of modern design and development techniques for all of the college's students.An new technology, mobile notification systems help to solve problems in many areas of life.In particular, students who disregard notices and circulars posted on notice boards were targeted in our study because of their predilection for mobile devices.This app keeps track of all college-related news and events, such as cancellations of training sessions or classes.
Addressing Cloud Computing Security and Visibility Issues

Network security has become a critical global issue for organizations due to the increasing cyber-attack rate.To enhance organizations' network security, this paper focuses on categorizing the security threat model, which enables examining the impact of threat classes rather than individual impacts.This paper reviews various threat categorization models and proposes criteria for classifying information system security hazards in cloud computing.It also investigates network security vulnerabilities and classifies security concerns using the CIA (confidentiality, integrity, and availability) triangle.Moreover, this paper explains fundamental concepts and procedures for carrying out various network security operations in cloud computing using both paid and open-source tools.This research aims to educate and enable organizations to reduce security threats while having full visibility and control of their infrastructure both in the cloud and on-premises.
Evaluation of AI Techniques for Detecting Deceptive Reviews in Cyberspace: A Study of Pre- and Post-COVID-19 Trends

With cyberspace’s continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer’s engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others.
Application of Minimum Bayes Factor to a Balanced Two-Way Anova with Random Effects

It is a common practice in statistical analysis to draw conclusions based on significance. P-values often reflect the probability of incorrectly concluding that a null hypothesized model is true; they do not provide information about other types of error that are also important for interpreting statistical results. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. In this paper, we focused on a minimum Bayes factor proposed by Held and Ott (2018) and applied it to a balanced two way analysis of variance (ANOVA) with random effects under three cases namely: Case 1: both factors are fixed; Case 2: both factors are random; Case 3: factor A is fixed and factor B is random. We realized that in all the three cases, considered the Bayes factor indicates weak evidence against the null hypothesis of zero variability in the effects of the levels of the factors as well as the interactions. This result is due to the conservative nature of the minimum Bayes factor.
On The Use of Analysis of Variance under Unequal group variances

In this study, we imposed Analysis of variances test (ANOVA) which use when we have more than two treatments or different levels of a single factors that we wish to compare then we assume homogeneity of variances across the groups being compared although most of the earlier works that have addressed the problem of testing equality of mean variance overestimates the appropriate variance and the test statistic becomes conservative. This is the well-known Behrens – Fisher problem. Then we are interested in comparing several treatments means in this work, we made use the analysis of variance under unequal variances when the groups variances differ. It will be very inappropriate to use the pooled sample variance as a single value for the variances, instead the sample harmonic mean of variances is proposed as an alternative to the pooled sample variance when there is heterogeneity of variances. The distribution theoretically and confirmed using simulation studies and this proposed harmonic mean of variance was , examined in this work and found useful for unequal variances.&#x0D; Data set from Kwara State Ministry of Health on the incidence of diabetes diseases for male patients was used to illustrate the relevance of our proposed test statistic.
An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction

Human motion prediction is one of the fundamental studies of computer vision. Much work based on deep learning has shown impressive performance for it in recent years. However, long-term prediction and human skeletal deformation are still challenging tasks for human motion prediction. For accurate prediction, this paper proposes a GCN-based two-stage prediction method. We train a prediction model in the first stage. Using multiple cascaded spatial attention graph convolution layers (SAGCL) to extract features, the prediction model generates an initial motion sequence of future actions based on the observed pose. Since the initial pose generated in the first stage often deviates from natural human body motion, such as a motion sequence in which the length of a bone is changed. So the task of the second stage is to fine-tune the predicted pose and make it closer to natural motion. We present a fine-tuning model including multiple cascaded causally temporal-graph convolution layers (CT-GCL). We apply the spatial coordinate error of joints and bone length error as loss functions to train the fine-tuning model. We validate our model on Human3.6m and CMU-MoCap datasets. Extensive experiments show that the two-stage prediction method outperforms state-of-the-art methods. The limitations of proposed methods are discussed as well, hoping to make a breakthrough in future exploration.
Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm

We, the publisher, removed Mackenzie Brown from the article: Bi, S., Li, Z., Brown, M., Xu, Y., &amp; Wang, L. (2022). Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm. EAI Endorsed Transactions on Scalable Information Systems, 10(2), e5. https://doi.org/10.4108/eetsis.v9i5.1567 after being notified by the Research Integrity and Governance Adviser of Edith Cowan University, that the author has never been affiliated with that institution. All the authors were informed about this fact and we did not receive any explanation about it. It was not clarified whether this was an "involuntary mistake" or a "false author." Following the COPE guidelines, Mackenzie Brown was REMOVED from this article because of “Potentially fake academic affiliation”.
Indirect RL with Function Approximation

Solving sequential decision problems with a large state space requires large storage memory and time-consuming computation. For example, there are over 1020 possible states in Backgammon and approximately 10170 possible states in Chinese Go. Moreover, many real-world tasks like autonomous driving has continuous state spaces. These largescale problems not only quickly deplete memory but also require a tremendous amount of expensive computation. When accurately learning all the tabular value and policy entries, this problem, which is known as the curse of dimensionality, becomes even worse. The curse of dimensionality refers to the exponential growth of a computational burden with respect to the dimension of state space and action space. The urgent need for solving large-scale problems has drawn great attention to the generalization technique.
Implementasi Kegiatan Pembelajaran Daring di Masa Pandemi Covid-19 dalam Menstimulasi Kemampuan Calistung Siswa Kelas II SD

The purpose of this study was to determine the implementation of online learning activities in stimulating the reading, writing and arithmetic (Calistung) ability of class II students of Panca Mukti Village, Central Bengkulu Regency. This research is field research, while the method used is descriptive. Qualitative research is a research procedure that produces descriptive data in the form of written or spoken words from people and observable behavior. The results of this study are the implementation of online learning activities in stimulating the ability of Calisutug class II children in Panca Mukti Village, Bengkulu Regency, online learning is carried out by utilizing the WhatsApp application, video calls and utilizing LKS learning resources relevant to the material to be delivered, stimulating children's Calisutug abilities Class II of Panca Mukti village, Central Bengkulu Regency, on the ability to read, write and count is still very low, both seen from the attitude during the learning process and the results in learning due to the lack of direct assistance from teachers due to the Covid 19 pandemic conditions. The implementation of online learning activities in stimulating the Calistung ability of grade II children in Panca Mukti village, Central Bengkulu Regency, on the ability to read, write and count is still very low, both in terms of attitudes during the learning process and results in learning due to lack of direct teacher assistance due to pandemic conditions Covid 19.&#x0D; Keywords : Implementation of Online Learning, Stimulating the Ability of Calistung
The Pendulum Motion Measured Digital Photogrammetry for a Centrifugal Pendulum Vibration Absorber

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Centrifugal Pendulum Vibration Absorber (CPVA for short) is used to absorb torsional vibrations caused by the shifting motion of the engine. It is increasingly used in modern powertrains. In the research of the dynamic characteristics of the CPVA, it is necessary to obtain the real motion of the pendulum to compensate the fitting performance of mathematical model. The usual method is to install an angle sensor to measure the movement of the pendulum. On the one hand, the installation of the sensor will affect its movement to a certain extent, so that the measurement results do not match the actual motion. On the other hand, the motion of the pendulum is not only the rotational motion around the rotational axis of the CPVA rotor, but also has translation relative to it. As a result, it is difficult to obtain accurate motion only by the angle sensor. We proposed a non-contact centrifugal pendulum motion measurement method. A high-speed camera is used to photograph the motion of the CPVA. For two adjacent images recording its motion, we use the ORB descriptor with rotation invariance to obtain the key points of the images. The Brute-Force Matcher(BF Mathcer) is used to match these points. Then, we calculate the rotational motion of the absorber rotor and the translation of the centrifugal pendulum relative to the rotor after peeling off the rotational motion around it. The method can obtain the motion of the centrifugal pendulum without affecting the motion of the CPVA. It is a feasible method without installation limitation of sensors to study the dynamic characteristics of the CPVA.&lt;/div&gt;&lt;/div&gt;