Reviewerly/exHarmony huggingface dataset:
Analisis Transitivitas Teks Takepan Banyuurung dan Relavansinya terhadap Pembelajaran Sastra

The problem studied in this study is the transitivity analysis of Banyuurung text. Based on the logic of this problem, it is also the problem in this study that describes the transitivity system and its relevance to learning literature in schools. The approach used in this study is descriptive qualitative and based on the theory of Systemic Functional Linguistics (LFS). The method of data collection is done through documentation of transcripts, studies, and notes as a technique. The data analysis was carried out using identification techniques and the results of data analysis used the description method, describing and describing the data that had been collected or existed. The research findings show that the transitivity system in Banyuurung text (mental process) is 60 %) and the process of being is 60%. In addition to the process of being, goals, actors, behavior, phenomena, sensing with a total of 20%.
The Impact of Innovation Adoption of Emerging Digital Technologies within a collaborative ecosystem on Firm Innovation Performance- Focus on Emerging Economies (Middle East, Africa, and Asia)

ABSTRACT: In today's fast-paced and ever-changing world, innovative technologies' rapid emergence and integration profoundly impact organisational strategies, the industry landscape, and overall economic and societal development. This article explores the adoption of specific emerging digital technologies (Artificial Intelligence (AI), Industry 4.0( I4.0 ), and the Internet of Things (IoT). It proposes a comprehensive framework to enhance firm innovation performance in organisations. A deep understanding of the future commercial utilisation of these technologies is crucial for informed decision-making. Acknowledging the interconnected nature of emerging technologies, this research emphasises the importance of collaboration among various stakeholders such as businesses, academic institutions, research organisations, regulatory bodies, and government agencies. This collaborative effort forms a unique and interdependent ecosystem essential for effectively applying, adapting, and improving emerging digital technologies. This study highlights perspective and limitations, particularly in emerging economies, by investigating the determinants that impact their implementation, mainly focusing on emerging economies. A robust framework is developed to address the research objectives by combining existing technology models, including the Diffusion of Innovation Technology-Organisation-Environment and Technology Acceptance Model. A quantitative research survey targeted managers and above in large enterprises and multinationals within the Middle East, Africa, and Asia manufacturing industries. The collected responses (n=153) were analysed using Structural Equation Modelling and Chi-Square methods. The findings of this study emphasise the advantages of the collaborative adoption of emerging digital technologies in improving firm performance. By bridging the gaps in integrating multiple emerging technologies within the innovation ecosystem, organisations can enhance their innovation capabilities and leverage them for sustainable competitive advantage. This comprehensive framework provides valuable insights and practical guidance for organisations seeking to navigate the complex landscape of emerging digital technologies and maximise their innovation potential.
nekton: A Linearizability Proof Checker

Abstract is a new tool for checking linearizability proofs of highly complex concurrent search structures. The tool’s unique features are its parametric heap abstraction based on separation logic and the flow framework, and its support for hindsight arguments about future-dependent linearization points. We describe the tool, present a case study, and discuss implementation details.
Tips dan Trik Penulisan Penelitian Tindakan Kelas di Lembata

Classroom action training begins with the problem of teachers who are still minimal in writing. The service team found that many teachers had not been able to combine observations in class. This research is a way for teachers to research in the classroom to find problems in the learning process. The teacher found many problems related to learning, namely process design, media, learning techniques, and learning outcomes. To realize good classroom action research techniques, training is done. Some teachers have not been able to write a good scientific paper. So the service team trains skilled teachers in writing. The service method begins with team formation, formulation of goals, and communication with the head of the Ministry of Religion office, then the analysis of needs in the field, determination of solutions to service problems and implementation. This training produces groups of writer teachers according to their respective fields. The team found already teachers who made classroom action research, but difficult in pouring it into the article. So that the team assists in writing classroom action research articles that will be published in journals. Follow-up assistance is carried out in the Whatsapp Messenger group.
That’s BAD: Blind Anomaly Detection by Implicit Local Feature Clustering

Abstract Recent studies on visual anomaly detection (AD) of industrial objects/textures have achieved quite good performance. They consider an unsupervised setting, specifically the one-class setting, in which we assume the availability of a set of normal (i.e., anomaly-free) images for training. In this paper, we consider a more challenging scenario of unsupervised AD, in which we detect anomalies in a given set of images that might contain both normal and anomalous samples. The setting does not assume the availability of known normal data and thus is completely free from human annotation, which differs from the standard AD considered in recent studies. For clarity, we call the setting blind anomaly detection (BAD). We show that BAD can be converted into a local outlier detection problem and propose a novel method named PatchCluster that can accurately detect image- and pixel-level anomalies. Experimental results show that PatchCluster shows a promising performance without the knowledge of normal data, even comparable to the SOTA methods applied in the one-class setting needing it
Development of a Secure Authentication System Using 5G for Smart Transportation System and Road Safety

Abstract The Internet of Vehicular Things (IoVT) field is expanding rapidly through the application of advanced technologies that enable the connection of vehicles, infrastructure , and other entities to the Internet. In addition to generating valuable data-driven insights, this development offers many advantages, such as enhancing safety, efficiency, convenience, and delivering personalized services. IoVT data is sensitive and requires secure storage and transmission, and if this information falls into the wrong hands, it could be misused for malicious purposes such as theft, fraud, or stalking. An unauthorized access to IoVT data has the potential to jeopardize the safety and security of vehicles and their passengers. Therefore, it is vital to establish a robust security mechanism to guarantee secure communication, ensure vehicle safety and integrity, facilitate efficient traffic management, provide emergency services, protect privacy and data, prevent unauthorized access, and to ensure compliance with regulations. To address the security requirements of IoVT, this research presents an innovative group-based authentication &amp; key agreement protocol designed explicitly for IoVT, and strives to achieve the core security objectives within the 5G networks. The proposed protocol successfully fulfills crucial security objectives in IoVT, including subscriber privacy, key confirmation , authentication &amp; key agreement, perfect forward/backward key secrecy, and session unlinkability. The security claims of the protocol underwent thorough examination using the AVISPA tool and BAN logic, confirming its ability to withstand diverse cryptographic attacks. Additionally, a mathematical investigation has affirmed the protocol’s suitability for IoVT devices, as it successfully achieves its objectives while minimizing signaling overhead, bandwidth usage, and computational burden.
Reconsideration and Proposal of Development Models in Projects- “Quasi” Development Models: Quasi-Waterfall and Quasi-Agile

Diverse development models, including waterfall development, iterative development, and agile development, have been put forth and implemented across real-world contexts. When engaging in discussions on project management, the examination and exploration of development models assume paramount importance and are integral. This paper embarks upon an investigation and scrutiny of these development models, culminating in the proposition of "Quasi" Development Models: Quasi-Waterfall and Quasi-Agile.
References

Understanding Society and Knowledge proposes that knowledge rather than nature, violence, or power provides the basis of and the driving force behind human action in modern society. It demonstrates how the legally enforced restricted use of knowledge enables the transformation of the knowledge society into knowledge capitalism.
Testing your case

Once you have drafted a complete case and Instructor’s Manual or Teaching Note (IM/TN), the next step is to get peer feedback and test your work out on a real class. We first recommend getting some initial peer feedback on your case so that the in-class test is as effective as possible. Recruit a colleague to either attend your in-class test or to try out your case themselves with you observing and taking notes. Following your case trial, make any recommended revisions to improve the case and IM/TN. By the end of the testing process, you should feel that another instructor who did not write the case would be able to teach the case effectively using your materials. Keep in mind that you may need to test your case several times if it is substantially revised during the review process. We provide a checklist of common review points for case and IM/TNs.
Bootstrap Approach for Principal Component Analysis Method; Cases studies

Analyzing big data poses a great challenge for numerous researchers to explore the data structure. Dimension reduction methods can be used to reduce data dimensionality, taking it from occupying a high-dimensional space to existing in a lower-dimensional space while retaining as much information as possible. Principal Component Analysis is one of the most popular used to reduce the dimensional space. The bootstrap sample is obtained by randomly sampling n times with replacement from the original sample, the method provides easy tool to understand the interactive component and develop the process. There are not enough researches discussed the stability of PCA method using bootstrap method. In this paper, the bootstrap method is used to analyze the stability of PCA results and to estimate the number of PCA in efficient way. The method is used to estimate the number of PCA that needed to classify the data set, and the effectiveness of the discussed techniques is demonstrated through real data sets.
PP-JPEG: A Privacy-Preserving JPEG Image Tampering Localization

The widespread availability of digital image processing softwares has given rise to various forms of image manipulation and forgery which can pose a significant challenge in different fields such as law enforcement, journalism etc,. it can also lead a privacy concern. We are proposing a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for purpose of Investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images if the forged portion (dummy image) has a compression quality different from the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10x10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.
A Study on The Uses of Blockchain in International Trade

Along with the constant change in living conditions and needs, technological developments have evolved rapidly in this direction and started to become widespread. One of these technological developments is about "money", which has an important place in human life. After the financial crisis in 2008, the introduction of the digital asset called Bitcoin led to discussions on the forms and functions of money and the initiation of studies in this direction. In the process, the use of blockchain technology, which constitutes the infrastructure of Bitcoin, was developed and went beyond just the exchange of assets in the digital environment, and the technology also showed itself in different fields such as finance, supply chain, health, real estate, government affairs. The potentials of blockchain technology have become more evident in international trade, as in many areas we have mentioned over time. The aim of this study is to discuss blockchain technology and the structures existing in the ecosystem in a conceptual framework, and to discuss the usage areas and potential advantages/disadvantages of blockchain technology in international trade and present information about the technology. In this context, firstly the blockchain technology and then the historical development process are mentioned, and the researches about the blockchain technology are presented by giving information about the technology in the conceptual framework. At the end of the study, it was emphasized that it would be beneficial to recognize this new technology, especially by the people involved in the workforce, to increase awareness of this new technology, and to accelerate the harmonization process with international trade, by giving the opportunity to the innovations brought by the blockchain. The research conducted in this study is intended to serve as a guide for future studies.
Efficiency Assessment of MapReduce Algorithm on a Serverless Platform

The MapReduce programming approach can divide large-scale data processing tasks into smaller, independent tasks, run those tasks concurrently on server clusters, and then integrate the outputs of those tasks to get the final result. Considering the wide application of MapReduce in massive data processing, this paper applies it to the serverless platform emphasizing ideas and services for realization simulation and applies it to the word count experiment. In the experiment, MapReduce is implemented on Alibaba Cloud and validated using a word count experiment of about 11K words. Its execution time on the platform under different CPU cores, memory configurations, and the number of workers is validated. By changing different platform configurations, it is concluded that compared to the number of workers, the size of CPU core has a huge impact on the response time, and the memory configuration does not affect the execution time of the model to a certain extent. By adjusting the model parameters in the experiment, the serverless implementation has a high efficiency compared with the non-serverless MapReduce model. The relationship between implementation efficiency and platform configuration provides references for optimizing the application of the MapReduce model on the serverless platform. Moreover, it may inspire the configuration of serverless hardware support in the future. At the same time, it further reflects the serverless platform's optimization of resource utilization efficiency in machine learning training.
Handwritten Digit Recognition Based on Convolutional Neural Network

Handwritten digit recognition is a research direction of computer image processing technology, which is how to use computer to recognize handwritten digits in different scenarios. Handwritten digit recognition is widely used in the Internet, education, finance, postal services, transportation and other industries. Thus, based on convolutional neural networks, this paper proposes a handwritten digit recognition model, which uses the Mnist dataset for learning and training. The simulation experiment results show that the recognition accuracy of the model is as high as 98%, and the model can be considered as good.
Wide-Bandwidth and High-Sensitivity Measurement Technique of Intrinsic Modulation Response of Semiconductor Lasers

We propose and demonstrate a measurement technique of the intrinsic modulation responses of semiconductor lasers with high sensitivity in a wide modulation frequency range. The technique is based on the two-tone optical modulation and lock-in detection of an optical spectrum having modulation sidebands. The measured modulation response of a conventional 1.55-mm distributed feedback laser indicates that the lock-in detection improves the signal-to-noise ratio by 22 dB. This significant improvement allows measurement of the modulation response up to 75 GHz even though the 3-dB bandwidth of the distributed feedback laser is only 11 GHz. Our technique is suitable for evaluation of the intrinsic modulation response of wide-bandwidth semiconductor lasers free from device parasitic effects.
Participatory Design for Whom? Designing Conversational User Interfaces for Sensitive Settings and Vulnerable Populations

Conversational User Interfaces (CUIs) are becoming increasingly applied in a broad range of sensitive settings to address the needs and struggles of vulnerable or marginalized users. Sensitive settings include, for instance, CUIs mediating the communication difficulties of people with dementia or supporting refugees to cope with new cultural practices as a chatbot on a government website. While researchers are increasingly designing CUIs for such sensitive settings, methods and participatory design approaches to address vulnerable user groups’ highly sensitive needs and struggles are sparse in research thus far. This workshop aims to explore how we can design CUIs for and in sensitive settings with vulnerable users in mind through the participatory design process. We aim to establish a working definition of vulnerability, sensitive settings, and how practice-oriented design of CUIs can be inclusive of diverse users.
Academic scientific schools of the Far East: sociological approach to the study of formation and development

In 2022, the Far-Eastern branch of the Russian Academy of Sciences celebrated its 90th anniversary. The Far-Eastern academic science is a notable education on the world scientific map. The world recognition of Far-Eastern academic science was brought by the works of the outstanding Far-Eastern scientists, creators and leaders of scientific schools, many of whom have no analogues in the world. The birth of scientific schools is associated with the names of outstanding scientists who have made a significant contribution to the methodology for the development of promising, most relevant areas of science. In addition, the science school is a good communication channel for the teacher's personal communication with their students, continuers of scientific traditions, research methodology and the main achievements of the founder of the school. At present, there is a need to prepare a reference book that will reflect the main milestones in the history and activities of scientific schools and scientific areas of the Far-Eastern branch of the Russian Academy of Sciences. When organizing the work on this topic, it is necessary to imagine not only what the phenomenon of scientific school is and how it should be studied, but also to identify how many scientific schools exist in the Far Eastern Branch of the Russian Academy of Sciences, who are their founders and leaders, to identify the most productive heads of scientific schools who have trained a significant number of students - candidates and doctors of sciences. This article is a kind of guideline for researchers in understanding the complex and multifaceted history of the Far-Eastern academic scientific schools.
A Comparative Study of Penalized Regression and Machine Learning Algorithms in High Dimensional Scenarios

With the prevalence of big data in recent years, the importance of modeling high dimensional data and selecting important features has increased greatly.High dimensional data is common in many fields such as genome decoding, rare disease identification, and environmental modeling.However, most traditional regression machine learning models are not designed to handle high dimensional data or conduct variable selection.In this paper, we investigate the use of penalized regression methods such as ridge, least absolute shrinkage and selection operation, elastic net, smoothly clipped absolute deviation, and minimax concave penalty compared to traditional machine learning models such as random forest, XGBoost, and support vector machines.We compare these models using factorial design methods for Monte Carlo simulations in 540 environments, with factors being the response variable, number of predictors, number of samples, signal to noise ratio, covariance matrix, and correlation strength.We also compare different models using empirical data to evaluate their viability in real-world scenarios.We evaluate the models using the training and test mean squared error, variable selection accuracy, β-sensitivity, and β-specificity.We found that the performance of penalized regression models is comparable with traditional machine learning algorithms in most high-dimensional situations.The analysis helps to create a greater understanding of the strengths and weaknesses of each model type and provide a reference for other researchers on which machine learning techniques they should use, depending on a range of factors and data environments.Our study shows that penalized regression techniques should be included in predictive modelers' toolbox.
Pengaruh Kegiatan Pembiasaan Literasi Dasar Terhadap Minat Baca Buku Fiksi Siswa Kelas VII SMP Negeri 137 Jakarta

This research is a quantitative study that aims to determine whether there is an effect of basic literacy habituation activities on interest in reading fiction books for class VII students at Junior High School 137 State of Jakarta. The population in this study were 214 students and a sample of 140 students, who were taken using simple random sampling technique. The variables used in this study are Basic Literacy Habituation Activities (X) and Interest in Reading Fiction Books (Y). The t test produced a sig value of 0.000 &lt;0.05 and tcount 10.117 &gt; ttable 1.977, which means that there is a significant influence between basic literacy habituation activities on students' interest in reading fiction books. The results of the correlation test obtained the value of the correlation coefficient r = 0.653, and it is known that the effect value of the basic literacy habituation activity variable on students' interest in reading fiction books is 42.6%.