Reviewerly/exHarmony huggingface dataset:
Robust Recursive Least-Squares Wiener Filter for Linear Continuous-Time Uncertain Stochastic Systems

For linear continuous-time systems with uncertainties in the system and observation matrices, an original robust RLS Wiener filter is designed in this study. The robust RLS Wiener filter does not assume norm-bounded uncertainty for the system and observation matrices, in contrast to the robust Kalman filter. In the design of the robust RLS Wiener filter, the degraded signal, affected by the uncertainties in the system and observation matrices, is modeled by an autoregressive (AR) model. The system and observation matrices for the degraded signal are formulated from the relationship between the AR model of the degraded signal and the state-space model. Estimation formulas for the system and observation matrices are proposed in Section 2. The robust filtering problem is introduced based on the minimization of the mean-square value of the filtering errors for the system states. The robust filtering estimate is given as an integral transformation of the degraded observations using the impulse response function. The integral equation that an optimal impulse response function satisfies is given in Section 3. Theorem 1 presents a robust RLS Wiener filtering algorithm starting from this integral equation. The proposed robust RLS Wiener filter outperforms the existing robust Kalman filter regarding estimate accuracy, as shown by a numerical simulation example.
Ideological Positioning in Covid-19 News Reportage in Ghana

This study investigates the different ideological positions in news reportage on COVID-19 between April to August, 2020, reported on Citi TV, Joy News and TV3. The research examines lexical choices in the news that enact ideological positions following the assumptions of Critical Discourse Analysis (CDA) as the main instrument of analysis for the news reports. It became evident from the analysis that the ideological positioning in the three TV stations are represented differently. Whereas Citi TV tends to use lexical choices to minimize the seriousness of the pandemic, Joy News uses lexical choices to hold the government and stakeholders accountable to affected patients. Lexical choices used by TV3, on the other hand, employs words to appeal to Ghanaians to adhere to safety protocols by providing facts from credible sources. The news reports on COVID-19 demonstrates how the three TV stations reported on the same pandemic in vastly different ways through particular uses of lexicon that reflected their differing ideological positions and national interests.
Artificial Intelligence used in Indian Economy Development and Management

The rapid advancement of Artificial Intelligence (AI) has significantly transformed industries across the globe, including the economic landscape of India. This research paper explores the multifaceted role of AI in the development and management of the Indian economy. With a focus on key sectors such as agriculture, healthcare, manufacturing, and finance, this paper provides a comprehensive analysis of how AI technologies are reshaping traditional paradigms and propelling India towards economic growth and sustainability. In the agricultural sector, AI-driven solutions are improving crop yields, resource management, and supply chain optimization. Through precision farming techniques, AI enables farmers to make data-driven decisions, thereby enhancing productivity and reducing resource wastage. Furthermore, AI-powered weather forecasting systems aid in mitigating climate-related risks. Manufacturing, a cornerstone of India's economy, has witnessed a revolution with the integration of AI-powered robotics and automation. This has led to increased efficiency, reduced costs, and improved product quality, making Indian manufacturing globally competitive. Moreover, this research paper delves into the opportunities and challenges of AI adoption in India, taking into account factors such as infrastructure development, skill enhancement, and international collaborations. It highlights the importance of a cohesive approach involving government, industry, and academia to harness the full potential of AI for economic development.
Factsheet on Artificial Intelligence in Nigeria

&lt;p&gt;Factsheet on Artificial Intelligence in Nigeria by Jake Okechukwu Effoduh for Paradigm Initiative, Nigeria.&lt;/p&gt;
Practical considerations in using a binary pseudorandom array for instrument transfer function calibration

Binary pseudo-random array (BPRA) artifacts are useful devices for calibrating the instrument transfer function (ITF) of interferometric microscopes and other optical and non-optical surface and wavefront measurement instruments. The intrinsic white noise character of the power spectral density function of the artifact simplifies the deconvolution of the ITF from the measured power spectral density (PSD). However, resampling of the BPRA intrinsic artifact features with the measurement tool’s specific sampling pattern modifies the white noise character of the intrinsic spectrum and needs to be accounted for in the ITF-based data deconvolution process. We have developed an analytic solution to the spectrum of a resampled one- and two- dimensional BPRA. The resultant nominal PSD function is a simple two-parameter cosine function with a period equal to the resampled pixel width. A transfer function model for interferometric microscopes that incorporates this function, along with an ITF that includes aliasing effects and variable numerical aperture (NA), wavelength, and obscuration factor, is used to fit to the BPRA PSDs measured by an interference microscope for a range of objective and zoom lens magnification combinations.
Probing Respiratory Care With Generative Deep Learning

This paper combines design, machine learning and social computing to explore generative deep learning as both tool and probe for respiratory care. We first present GANspire, a deep learning tool that generates fine-grained breathing waveforms, which we crafted in collaboration with one respiratory physician, attending to joint materialities of human breathing data and deep generative models. We then relate a probe, produced with breathing waveforms generated with GANspire, and led with a group of ten respiratory care experts, responding to its material attributes. Qualitative annotations showed that respiratory care experts interpreted both realistic and ambiguous attributes of breathing waveforms generated with GANspire, according to subjective aspects of physiology, activity and emotion. Semi-structured interviews also revealed experts' broader perceptions, expectations and ethical concerns on AI technology, based on their clinical practice of respiratory care, and reflexive analysis of GANspire. These findings suggest design implications for technological aids in respiratory care, and show how ambiguity of deep generative models can be leveraged as a resource for qualitative inquiry, enabling socio-material research with generative deep learning. Our paper contributes to the CSCW community by broadening how generative deep learning may be approached not only as a tool to design human-computer interactions, but also as a probe to provoke open conversations with communities of practice about their current and speculative uses of AI technology.
Constraints In The Application of Single Truck Identification Data For Operations At Tanjung Emas Port, Semarang

Single Truck Identification Data is an electronic data collection system for every truck operating at a designated port to support activities at the port. Single Truck Identification Data will later become the single identity of each truck, with an electronic-based system connected to the port management IT system containing a database covering the technical feasibility of trucks and their drivers, including data on truck vehicle police numbers and transportation company owners. However, there are still many trucks and companies that have not registered their companies and have not pocketed a Single Truck Identification Data card; therefore, there are obstacles faced by business actors so that until now they have not registered their companies. The lack of preparation of trucking companies in using the system and the lack of knowledge about the Single Truck Identification Data system are obstacles to implementing the system. The factor of implementing the system is to facilitate the flow of goods at the port. Efforts can be made to reduce existing obstacles by providing socialization and technical guidance to truck drivers and operators, as well as improving services for trucking companies to register their fleets.
Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques

The rise of digital transactions and electronic payment systems in modern financial management has brought convenience but also the challenge of credit card fraud. Traditional fraud detection methods are struggling to cope with the complexities of contemporary fraud strategies. This study explores the potential of machine learning, specifically the XGBoost (eXtreme Gradient Boosting) algorithm, combined with data augmentation techniques, to enhance credit card fraud detection. The research demonstrates the effectiveness of these techniques in addressing imbalanced datasets and improving fraud detection accuracy. The study showcases a balanced approach to precision and recall in fraud detection by leveraging historical transaction data and employing techniques like Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN). The implications of these findings for contemporary financial management are profound, offering the potential to bolster financial integrity, allocate resources effectively, and strengthen customer trust in the face of evolving fraud tactics.
The Impact of Digital Talent Management on Employee Performance with Individual Innovation Behavior as Intervening Variable at PT Dakwah Inti Media

The purpose of this study was to analyze the effect of digital talent management on employee performance with individual innovation behaviour as an intervening variable. This research is causality research with a quantitative approach. The sampling technique used saturated samples with a total of 54 respondents at PT Dakwah Inti Media. The statistical analysis used in this research is partial least square with the help of SmartPLS 3.2.9 software. The result of the analysis obtained from this study indicates that digital talent management has a positive and significant effect on employee performance; digital talent management has a positive and significant effect on individual innovation behaviour; individual innovation behaviour has a positive and significant effect on employee performance; individual innovation behaviour in this study can mediate the effect of digital talent management on employee performance.
Low-Power human face tracking system for art installations at low ambient light

This paper aims to create an interactive human-machine art exhibition solution for a light and shadow art installation through embedded edge computing. Originally, this device used a microcontroller for simple microcontrol of the stepper motor, which did not meet the artists’ needs. Therefore, we added a high-sensitivity lens module and an embedded NVIDIA edge computing platform to the device and equipped it with an interactive method for efficient real-time face recognition and tracking at night through YOLOv4 tiny. We improve the YOLOv4-tiny-3l model by using various hyperparameter adjustments and image preprocessing methods to increase the diversity and generalization capability of the dataset through self-training. This allows the YOLOv4-tiny-3l network structure model to improve face recognition speed by 5–10% and average accuracy by 1–5% at a training resolution of 320×320 compared to the YOLOv4-tiny network structure at a training resolution of 416×416.
Optimisasi Algoritma Genetika dengan Particle Swarm Optimization (PSO) untuk Sistem Rekomendasi Diet Gizi bagi Penderita Diabetes

Diabetes, especially diabetic nephropathy, is a global health problem that is increasing in prevalence. This disease can cause various serious complications and even death. Despite the high cure rate associated with diabetes, it is important to improve the human body's immune system to reduce the risk of developing diabetes or diabetic nephropathy. One approach that can help is maintaining a diet with good nutritional coverage. This research aims to develop an artificial intelligence (AI) system that can provide recommendations for a good nutritional diet menu for diabetes sufferers. We propose the use of well-known genetic algorithms in decision making. However, to improve the accuracy and efficiency of the genetic algorithm, we will optimize it using the Particle Swarm Optimization (PSO) algorithm. The research method used is an experimental method, where we will conduct experiments to test the performance of the optimized genetic algorithm. It is hoped that the results of this research can be used as a basis for making scientific publications in accredited national journals as well as product patents for food menu recommendation systems for diabetes sufferers. The main contribution of this research is improving the performance of the genetic algorithm through the use of the PSO algorithm, which will help increase the accuracy of the nutritional diet recommendation system. In this way, it is hoped that the results of this research can provide significant benefits in efforts to prevent and manage diabetes and improve the quality of life of diabetes sufferers.
An Exploratory Study on Cooperative Crisis Management in Police Activities

To minimize the damage caused by natural and social disasters, the government will come up with a reform of the existing disaster safety management system. Establishing an organic system of cooperation between police, firefighting, and local governments to enhance the on-site response will be in the central issue of the reform. Furthermore, the rapid changes in security environment and the increased possibility of uncertain large disasters make it necessary to systematically enhance police activities at each stage of crisis management. In addition, a leadership approach centered on police activities and police management capabilities will be crucial for successful crisis management. By examining the crisis management system in the U.S. and Germany, this paper draws implications for the role of police in the crisis management system in South Korea and claims that cooperative police leadership is essential for that system. Moreover, this paper provides with a list of capabilities necessary for police activities in crisis management as a basis for the future development of police capabilities model for crisis management.
Camera Model Identification with Convolutional Neural Network

With the recent developments in multimedia, the forging of images has become an increasing threat for forensics. This study proposes a method using deep learning to trace an image back to its source camera model. In this study the classification performed for a set of 7 camera models but it is extendable and can be used between any numbers of models. It depends on training data given to the algorithm. Two techniques were used for simulations; Dense Neural Networks (DNN) and Convolutional Neural Networks (CNN). Both of these are state of the art techniques yield a strong model. In results the comparison of both the techniques are shown. Result has shown that CNN provides more accurate results.
Fuzzy Based Sentiment Analysis of E-Com and S-Com Data Using Transformer Neural Network

In the context of existing sentiment analysis, comments are classified as positive, negative, or neutral. In some cases, sentimental comments contain a dual meaning within a single sentence. However, the sentence also includes both positive and negative vibes. In such cases, negative comments are classified as neutral or ignored (for example, the dress appears to be really nice to wear, but it's worth the price because it's simple and there's no heavy work). Based on TF-IDF technique, this research study proposes a fuzzy based weighted sentiment classification by using transformer neural networks. Based on the data set, the system will analyze both the positivity and the negativity of the sentences by calculating their weights. In the proposed system, the weighted value is used to classify positives and negatives with a minimum threshold level. If the calculated weights are the same mean, then the system is considered neutral. This system enables to find out the average weighted factor of positive comments for product promotion. As an alternative, the seller can indirectly improve quality by specifying negative average weighted factors. Overall, the system was evaluated in terms of Precision & Accuracy and produced 95.6% & 96% respectively, and its results were better than those of other existing systems.
FEDOR EGOROVICH PAKTOVSKII (1859 - ABOUT 1922), A TEACHER AND PUBLIC FIGURE OF EDUCATIONAL INSTITUTIONS IN SARATOV, KAZAN, TAMBOV, KURSK AND KHAR’KOV PROVINCES

Based on a wide range of historical sources, including published reference and anniversary editions, periodicals and documents from the collections of the Russian State Historical Archives and the Department of Manuscripts of the Russian State Library, the professional biography of Paktovskii Fedor Egorovich, a teacher, philologist and public figure, was restored. The research paper introduces previously unknown photographs of him and his family into scientific circulation for the first time, and also presents the attribution of an exhibit, i.e., a book by F.E. Paktovskii, kept in the funds of the Museum of History of the National Research University “BelSU”. In accordance with the geography of his career movements, five main periods of his life (Saratov, Kazan, Morshansk, Belgorod, Khar’kov) were identified and studied in chronological order. It is established that the professional path of F.E. Paktovskii is a movement from the position of a teacher to the position of a gymnasium director, and his social activism was aimed not only at the development of educational institutions, but also the cities in which he served. The research activity of F.E. Paktovskii, as a philologist, that he practiced for many years, is also of interest to researchers. Based on the information from the materials of the periodical press, it was possible to find that he was loved by his students and the intelligentsia
A CENTURY-LONG JOURNEY. FROM THE “SIBARCHIVE” BULLETIN OF THE 1920S TO THE SCIENTIFIC AND HISTORICAL JOURNAL OF SIBERIAN ARCHIVISTS “SIBERIAN ARCHIVE”

The article presents results of the analysis conducted on the basis of archival materials of the bulletin “Sibarchiv” – the first in Siberia periodic information archival publication, which played an extremely important role in establishing the information exchange of archival institutions in the vast territories of the Siberian Region, in popularizing archives, attracting them to scientific work. All three and a half years’ worth of issues of the bulletin are considered in detail and the most important topics and questions raised in it since 1928 highlighted. The outstanding role of V.D. Wegman, the head of the Sibarchive in saving documentary and archival materials from plunder and destruction, in organizing provincial archival departments, in attracting scientifically erudite, dedicated archiving specialists for work in the archives of Siberia is indicated. It is shown how the experience of the bulletin “Sibarchiv” turned out to be in demand by archivists of the late 20th century – early 21st century. The examples given in the article prove that modern versions of the periodic archival press – the bulletin “Novosibirsk Archival Bulletin” and the scientific and historical electronic journal “Siberian Archive” – have absorbed interesting and effective forms and methods of work from their predecessors, revived and preserved the best traditions, remained in line with the current concerns of the professional community of historians and archivists in Siberia.
FORMATION OF UNIVERSAL COMPETENCIES AMONG STUDENTS THROUGH THE IMPLEMENTATION OF THE “BEST ASSET” PROGRAM

In the context of changing trends in the development of Russian society, the information war for the “minds” of young people, the problem of education and prevention plays an important role at the state level. The recent tragic events in our country associated with the manifestations of “schoolshutting” and “collumbine” prove the relevance of the problem of preventive education of young people. Today, such pedagogical categories as “education” and “prevention” should be considered in a single interconnected university environment, due to the increase in negative manifestations among young people. We believe that the mission of the university today is the formation of a safe educational and preventive environment, which will be aimed not only at the safety of students, but also at comprehensive, harmonious and professional development. For the organization of this environment, we offer a special pedagogical impact — the “Best Asset” program, which contributes to the formation of this environment and is a kind of tool for the assimilation of universal competencies among students. Since modern education in Russia is built on a competence-based approach, the essence of which is to acquire the necessary key competencies that satisfy the labor market. We believe that the “Best Asset” program as a means of organizing the educational and preventive environment of the university should meet modern realities and form universal competencies among students, which are so necessary for employers. The article discusses the formation of universal competencies among students through the implementation of the “Best Asset” program at the university.
PEDAGOGICAL MODELING AND EVALUATION OF THE FUTURE CIVIL SERVANT SOCIO-LEGAL POSITION FORMATION EFFECTIVENESS

Modeling the process of forming the socio-legal position of a future civil servant allows us to present the process taking into account its essential characteristics and possible risks. The logic of formation is represented by a process model consisting of target, methodological, socio-legal, content-technological and performance-diagnostic blocks. The target block of the process model highlights the leading direction of activity: to form the socio-legal position of the future civil servant. The methodological basis is the activity approach and its complementary principles of socio-legal orientation, resourcefulness, team design and visualization. In the socio-legal block, based on the norms of law and the content of the Federal State Educational Standard in the direction of training 38.03.04 State and Municipal Administration, the value, knowledge and behavioral dominants of the socio-legal position of the future civil servant are highlighted. The content and technology block represents the interaction of the subjects of the “teacher — future civil servant — regional employer” process at the stages of the work of the “Premier Project” Factory organized at the university. The effective diagnostic unit determines the correspondence of the components (value, knowledge, behavioral), criteria (axiological, epistemological, praxiological) and indicators (awareness, validity, stability) to the formation of the socio-legal position of the future civil servant. The result of the process model is the formed socio-legal position of the future civil servant. The evaluation of the effectiveness of the modeled process was carried out when the model was introduced into the educational practice of the university. The conducted diagnostics of the formation of the socio-legal position of the future civil servant revealed in the ascertaining experiment the predominance of low and medium levels of formation of the socio-legal position of the future civil servant in the educational community; in the forming experiment, an increase in the proportion of students who have a high level of formation of this quality.