Reviewerly/exHarmony huggingface dataset:
Analysis of E-Learning Activities as School Learning Media in the Era of Society 5.0 Using Big Data

To prepare the community to face Society 5.0, cooperation is needed from various parties, including the education sector. The development of the education sector is crucial today, as it provides optimal and quality educational services and facilitates technological advancements. Regarding educational technology, a system utilized to support learning and achieve desired outcomes, it is important to associate it with the Society 5.0 era. One form of technological development in education that can be used as a learning medium is e-learning. This research adopts a quantitative approach. The research subjects consist of secondary data from a Kaggle dataset titled "Students Adaptability Level in Online Education," which was collected by Md. Aktaruzzaman Pramanik and Nishat Ahmed Samrin. The data includes 530 students who participated in online learning at schools. The results of this research indicate that the development of information technology in the Society 5.0 era can be utilized by the education sector, such as through e-learning as a learning medium. The impact of these research findings can be observed in the quality of education in Indonesia, where all aspects of life coexist with digital media.
Development of a monitoring system for COVID-19 monitoring in early stages

Covid-19 is considered the most infectious virus today. Likewise, the struggle to mitigate the effects of the variants, the flexibility in some measures such as the use of face masks, the advancement of vaccination and prevention and self-care campaigns continue to be topics of research and of global interest. The world health authorities published that the disease was characterized by presenting the same symptoms as the flu along with a complex picture where in the most serious cases they lead to difficulty breathing due to pneumonia, sepsis and septic shock that can lead to death. Some systems implemented for taking body temperature such as thermographic cameras, digital thermometers, for the description of symptoms in the people they analyze at the time of carrying out the epidemiological fences are not enough, since they handle low precision, are taken in isolation, individually or randomly and is not suitable for characterizing interest groups. Then, establishing risk levels by measuring non-invasive variables can be considered inputs into prevention campaigns and a low-cost way of monitoring the community. This article shows the design of a non-invasive embedded device for the measurement of 5 priority variables for the detection of the risk of covid-19 infection. The proposed device was duly calibrated and synchronized for the acquisition of data from 594 people in the city of Bucaramanga, Colombia, who authorize the monitoring of the symptoms. The people must be in a state of rest to be able to acquire the data with great accuracy, in this way the data is entered into the system in charge of doing the monitoring analysis. Additionally, the implementation of an interface that allows the visualization of results, laying the foundations for the development of automatic learning techniques or models for the risk classification in future work.
Real-time image analysis in IoT-based home security system

Internet of Things (IoT) uses cloud-enabled data sharing to connect physical objects to sensors, processing software, and other technologies via the Internet. IoT allows a vast network of communication amongst these physical objects and their corresponding data. This study investigates the use of an IoT development board for real-time sensor data communication and processing, including images from a camera, as part of a custom-made home security system intended for the elderly for easy access.
Efficient hyperparameter optimization for ATR using homotopy parametrization

Deep learning has expedited important breakthroughs in research and commercial applications for next-generation technologies across many domains including Automatic Target Recognition (ATR). The success of these models in a specific application is often attributed to optimized hyperparameters: user-configured values controlling the model’s ability to learn from data. Tuning hyperparameters however remains a difficult and computationally expensive task contributing to deficient ATR model performance compared to set requirements. We present the efficacy of applying our developed hyperparameter optimization method to boost the effectiveness and performance of any given optimization method. Specifically, we use a generalized additive model surrogate homotopy hyperparameter optimization strategy to approximate regions of interest and trace minimal points over regions of the hyperparameter space instead of ineffectively evaluating the entire hyperparameter surface. We integrate our approach into SHADHO (Scalable Hardware-Aware Distributed Hyperparameter Optimization) a hyperparameter optimization framework that computes the relative complexity of each search space and then monitors the performance of the learning task over the trials. We demonstrate how our approach effectively finds optimal hyperparameters for object detection by conducting a model search to optimize multiple object detection algorithms on a subset of the DSIAC ATR Algorithm Development Image Database and finding models that achieve comparable or lower validation loss in fewer iterations than standard techniques and manual tuning practices.
Evaluation of Transformer-Based Models for Punctuation and Capitalization Restoration in Spanish and Portuguese

Punctuation restoration plays a key role as a post-processing task in various text generation methods, such as Automatic Speech Recognition (ASR), and Machine Translation (MT). Despite its importance, the results of ASR systems and other generation models used in these tasks often produce texts that lack punctuation, which is difficult for human readers and might limit the performance of many downstream text processing tasks for web analytics, such as sentiment analysis, sarcasm detection or hate-speech identification including stereotypes, sexism, and misogyny. Thus, there are many techniques for restoring text punctuation, but most solutions like Condition Random Field (CRF) and pre-trained models such as the BERT, have been widely applied. In addition, they focus only on English and on restoring punctuation, without considering the restoration of capitalization. Recently, there has been a growing interest in an alternative method of addressing the problem of punctuation restoration, which is to transform it into a sequence labeling task. In this sense, we propose a capitalization and punctuation restoration system based on Transformers models and a sequence labeling approach for Spanish and Portuguese. Both models obtained good results: a macro-averaged F1-score of 59.90% and overall performance of 93.87% for Spanish and macro-averaged F1-score of 76.94% and 93.66% overall performance for Portuguese. In addition, they are also able to restore capitalization, identifying proper names, names of countries and organizations.
Investigation of IoT applications in supply chain management with fuzzy hierarchical analysis

The IoT is currently growing rapidly and uses technologies such as smart barcode sensors, RFID, wireless communications, cloud computing, and more. The Internet of Things, in addition to being a revolutionary technology for all industries; has also demonstrated its potential in processes such as supply chain. Management, forecasting, and monitoring applications help managers improve the operational efficiency of their company distribution and increase transparency in their decisions. So more than ever, the benefits of using the Internet of Things are evident in the supply chain. The existence of comprehensive and valid information platforms is one of the requirements of supply chain management. Therefore, the most accurate use of integrated information devices such as Internet technology of objects in this part of the management of the organization is important. Coverage of this information accurately and in an instant facilitates matters and makes the process progress more transparent. To improve this process, cloud computing is used as a solution. In addition, other cloud computing capabilities can be used, such as facilitating object communication, integrating monitoring devices, and IoT storage, analyzing data, and paving the way for cyberspace to provide the customer with supply chain management. This requires a model that defines how Internet technology relates to objects, cloud computing, and supply chain management. The purpose of this study is to identify and prioritize IoT applications in the supply chain management sector with a multi-criteria decision-making approach. The results show that applications such as intelligent control and intelligent maintenance have the highest priorities.
Negative binomial mixed model neural network for modeling of pulmonary tuberculosis risk factors in West Java provinces

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts.
IMPACT OF ONLINE INSTRUCTOR SCAFFOLDING STRATEGIES ON STUDENTS’ SPEAKING PERFORMANCE

Students need to master oral communication skills during their university studies to prepare them for future working environment. However, many ESL students are facing difficulties communicating in English because they seldom use English for interaction outside their English classes. Additionally, they lack proper guidance from instructors on how to improve their speaking skills. Thus, this study aims to identify the types, usefulness, and impact of Online Instructor Scaffolding Strategies on students’ speaking performance. A mixed-method research design was utilised in this study. The instruments used in data collection included an open-ended questionnaire designed by the researchers, pre-test and post-test, recorded videos of students’ role-play, and instructor’s online feedback. The participants were 66 mixed-proficiency level students from a university in Malaysia. The findings revealed various useful online instructor scaffolding strategies enhanced students’ speaking performance. Post-test results showed that the Online Instructor Scaffolding Strategies have an impact on students’ speaking performance. The findings from this study are meaningful as Online Instructor Scaffolding Strategies can benefit students of all proficiency levels to improve their speaking performance.
Payload symbol-based nonlinear RF fingerprint for wireless QPSK-OFDM devices

Radio Frequency (RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity. To improve the performance of RF Fingerprint (RFF) based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.
Efficient Verification of Arbitrary Entangled States with Homogeneous Local Measurements

Abstract Quantum state verification (QSV) is the task of relying on local measurements only to verify that a given quantum device does produce the desired target state. Up to now, certain types of entangled states can be verified efficiently or even optimally by QSV. However, given an arbitrary entangled state, how to design its verification protocol remains an open problem. This study presents a systematic strategy to tackle this problem by considering the locality of what it initiates as the choice‐independent measurement protocols, whose operators can be directly achieved when they are homogeneous. Taking several typical entangled states as examples, this study shows the explicit procedures of the protocol design using standard Pauli projections, demonstrating the superiority of the method for attaining better QSV strategies. Moreover, the framework can be naturally extended to other tasks such as the construction of entanglement witnesses, and even parameter estimation.
Teaching Practice and Exploration of Basic Computer Education for Non Major in University

With the rapid development of computer information, Computer technology and products have been fully integrated into people's daily life.This requires people to have basic computer operation skills.As an agricultural and forestry university, it is necessary to pay attention to the cultivation of basic computer skills of college students in various majors in the process of training talents.As a professional teacher in universities, the author has worked hard in teaching for nearly 17 years.On the one hand, the author undertakes the professional teaching of Internet of Things Engineering, on the other hand, she undertakes the teaching of College Students' Computer Foundation.The author pays attention to the renewal of the teaching content of the course, and actively carries out the reform and practice in teaching.Our teaching team has accumulated rich teaching experience and methods after years of practical teaching.This paper mainly comes from the author's many years teaching experience in basic computer education.Firstly, the author systematically analyzes the problems and causes in the teaching process of computer basic courses.Members of the teaching team actively carry out curriculum teaching reform and practice, carrying out online and offline hybrid teaching.The ultimate goal of teaching is to constantly improve the teaching quality and effect, improve the students' practical ability and office automation level.Students from different majors increase their core competitiveness of employment through systematic learning.
A Weighting Model of Cybersecurity Parameters Used for Service Placement

Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks.&#x0D;
Application of the New Growth Theory to Financial Risk Management

The aim of this research was to look at certain trends in the New Growth Theory. A literature search study was done with the purpose of establishing linkages between the New Growth Theory on Financial Risk Management and other aspects of financial impacts. The findings indicate that economic development policy is affected in a number of ways by the New Growth Theory. This may include the generation of new knowledge, which is crucial for growth sustenance brought about by increasing demand for production due to an increase in the needs and wants of the population. All of the elements that encourage the formation of new knowledge, including research and development, the educational system, and entrepreneurship, require careful consideration on the part of policymakers. By attributing production growth to externalities produced by investments in human capital and technology, the new growth theory endogenizes the drivers of growth; some variations also incorporate policy variables. The new growth theories warn against overprotecting intellectual property rights, but they also suggest that in a situation where there is a significant and expanding information gap between wealthy and developing nations, some trade restrictions may be justifiable. The New Growth Theory is significantly correlated with the value of human capital, quality education, new knowledge, and the government's provision of incentives for the private sector's research and development (R&amp;D).
It Takes a Researcher to Know a Researcher: Academic Librarian Perspectives Regarding Skills and Training for Research Data Support in Canada

Objective – This empirical study aims to contribute qualitative evidence on the perspectives of data-related librarians regarding the necessary skills, education, and training for these roles in the context of Canadian academic libraries. A second aim of this study is to understand the perspectives of data-related librarians regarding the specific role of the MLIS in providing relevant training and education. The definition of a data-related librarian in this study includes any librarian or professional who has a conventional title related to a field of data librarianship (i.e., research data management, data services, GIS, data visualization, data science) or any other librarian or professional whose duties include providing data-related services within an academic institution. Methods – This study incorporates in-depth qualitative empirical evidence in the form of 12 semi-structured interviews of data-related librarians to investigate first-hand perspectives on the necessary skills required for such positions and the mechanisms for acquiring and maintaining such skills. Results – The interviews identified four major themes related to the skills required for library-related data services positions, including the perceived importance of experience conducting original research, proficiency in computational coding and quantitative methods, MLIS-related skills such as understanding metadata, and the ability to learn new skills quickly on the job. Overall, the implication of this study regarding the training from MLIS programs concerning data-related librarianship is that although expertise in metadata, documentation, and information management are vital skills for data-related librarians, the MLIS is increasingly less competitive compared with degree programs that offer a greater emphasis on practical experience working with different types of data in a research context and implementing a variety of methodological approaches. Conclusion – This study demonstrates that an in-depth qualitative portrait of data-related librarians within a national academic ecosystem provides valuable new insights regarding the perceived importance of conducting original empirical research to succeed in these roles.
A novel systems solution for accurate colorimetric measurement through smartphone-based augmented reality

Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alternative is to use digital images for colorimetric measurement, due to their portability and ease of use. However, image-based measurements suffer from errors caused by the non-linear image formation process and unpredictable environmental lighting. Solutions to this problem often perform relative color correction among multiple images through discrete color reference boards, which may yield biased results due to the lack of continuous observation. In this paper, we propose a smartphone-based solution, that couples a designated color reference board with a novel color correction algorithm, to achieve accurate and absolute color measurements. Our color reference board contains multiple color stripes with continuous color sampling at the sides. A novel correction algorithm is proposed to utilize a first-order spatial varying regression model to perform the color correction, which leverages both the absolute color magnitude and scale to maximize the correction accuracy. The proposed algorithm is implemented as a “human-in-the-loop” smartphone application, where users are guided by an augmented reality scheme with a marker tracking module to take images at an angle that minimizes the impact of non-Lambertian reflectance. Our experimental results show that our colorimetric measurement is device independent and can reduce up to 90% color variance for images collected under different lighting conditions. In the application of reading pH values from test papers, we show that our system performs 200% better than human reading. The designed color reference board, the correction algorithm, and our augmented reality guiding approach form an integrated system as a novel solution to measure color with increased accuracy. This technique has the flexibility to improve color reading performance in systems beyond existing applications, evidenced by both qualitative and quantitative experiments on example applications such as pH-test reading.