Reviewerly/exHarmony huggingface dataset:
Simple chain automaton random number generator for IoT devices

Abstract Random numbers are very important in many fields of computer science. Generating high-quality random numbers using only basic arithmetic operations is challenging, especially for devices with limited hardware capabilities, such as Internet of Things (IoT) devices. In this paper, we present a novel pseudorandom number generator, the simple chain automaton random number generator (SCARNG), based on compositions of abstract automata. The main advantage of the presented algorithm is its simple structure that can be implemented easily for very low computing capacity IoT systems, FPGAs or GPU hardware. The generated random numbers demonstrate promising statistical behavior and satisfy the NIST statistical suite requirements, highlighting the potential of the SCARNG for practical applications.
Characteristics of the shortwave-thermal infrared spectra of the Zhuxi superlarge tungsten deposit in Jiangxi Province and their exploration significance

Infrared spectroscopy, as an innovative exploration tool, enables rapid identification of alteration minerals, thus can facilitate an improved understanding of mineralization and enhance prospecting efficiency. This paper specifically applies this methodology to drill core samples from the Zhuxi Deposit, Jiangxi Province, the world's largest tungsten deposit. Using shortwave and thermal infrared spectroscopy, this study aims to identify spectral characteristics of typical altered minerals within the deposit, establish the mineral identification methods and preferred spectrum bands, and discern spectral characteristics of varied altered mineral combinations. The variations observed in the characteristic bands of these isomorphic minerals serve as indicators of changes in the ore-forming environment. Specifically, absorption features around 2190 nm are indicative of paragonite formation in low-temperature environments, while features around 2210–2225 nm signify the presence of phengite in high-pressure, high-temperature conditions. Absorption at 2260 nm is characteristic of Fe-rich chlorite, suggesting a low-temperature, reducing environment, whereas Mg-rich chlorite exhibits absorption at 2250 nm, indicative of a higher-temperature environment. Based on depth, the deposit can be classified into three types of alteration mineralization: greisen, garnet skarn, and diopside skarn. Shortwave infrared spectroscopy proves effective for identifying muscovite in greisen and altered minerals such as serpentine and chlorite in diopside skarn. Thermal infrared spectroscopy, on the other hand, is ideal for quartz identification in greisen and garnet in garnet skarn. Intriguingly, the study finds that the absorption characteristics of garnet can reflect the metal mineralization type of the deposit. The absorption valley wavelength of garnet varies from around 11290–11380 nm in non-mineralized or poorly mineralized samples, to 11230–11290 nm when scheelite mineralization dominates, and to 11180–11230 nm when both scheelite and chalcopyrite are present. These findings contribute significantly to our understanding of the mineralogical characteristics and genetic environment of the Zhuxi deposit. Moreover, they provide valuable insights into the potential application of infrared spectroscopy for skarn zonation and mineral exploration.
Model SOP IT Service Desk Berdasarkan Framework ITIL V3

IT services are critical to organizational success because of their ability to increase organizational competitiveness and flexibility. In managing quality IT services, IT Service Management (ITSM) is needed. ITSM is the implementation and management of quality IT services to meet business needs. To guarantee and support ITSM, an IT Service Desk (ITSD) function is required. ITSD is a functional unit consisting of a number of staff who are responsible for handling various IT service activities. The function of ITSD is to record and manage and resolve all incidents, problems, IT service requests, and IT service access rights by providing a central point of contact. Therefore, ITSD becomes very important in ensuring and supporting the management of IT services. In carrying out this ITSD function, it is necessary to develop standard operating procedures (SOPs) so that the implementation of work is more structured and measurable. Based on this, the researcher created an ITSD SOP model based on the ITIL V3 framework with a case study in the Pengembangan dan Penerapan Teknologi Informasi (PPTI) section at Universitas Dinamika. This ITSD SOP model produces 3 SOP which includes recording requests and complaints for IT services, analysis and actions, reviewing actions with descriptions of 7 Work Instructions, and 26 Work Records. This ITSD SOP model can serve as a guide in recording, managing, and resolving requests and IT service access management as well as problem-solving both incidents and IT service problems for the organization.
Research on video activity understanding based on deep learning

There are very rich individual and group activity in the video. It is very significant to excavate the characteristics of individuals and groups and analyze the interaction between them. In this paper convolution neural network and graph relationship are used to identify the activities of individuals and individuals in the video, and the accuracy rate and intersection ratio are used to evaluate, and good results are obtained after verification.
Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients

Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark’s Capital Region and Region Zealand during 2011–2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.
Examining Advantages, Reasons, and Issues of Ordering Online Food through Mobile Application

Abstract: Online Food delivery refers to the process when different restaurants, cafes, and food outlets deliver food or drinks to the customers according to their order placed by any mode to meet the exact demand and provide customer satisfaction and service of the best quality. Most people use their mobile phones to do the process; However, there are many advantages of ordering food through mobile-based apps; what are the reasons behind it, and what possible issues do customers face while ordering food online? This paper will provide information on those factors and discuss the most significant reasons, how those issues can be solved, what advantages customers get which motivate them to go online Food ordering instead of going out, and how Covid-19 impacted the online food ordering Industry. In the times of Covid-19, Online food ordering helped in different ways when people were not in a condition to cook food on their own and could not go out of the house to protect everyone.
A Questionnaire for Assessing Immersive Websites

Web-immersion – i.e., a deep sense of cognitive and perceptual absorption engendered by the content and interactive features of a website - plays an important role in our modern, digital world. Yet, this topic has received little attention from design and HCI scholars. The lack of tools to assess and measure immersion in the Web may severely limit our ability to understand the nature of such experience, thereby constraining future research in the area. To address this issue, we designed a questionnaire for assessing immersive websites and conducted a preliminary evaluation on it. In this article, we outline the questionnaire design and report on findings from a preliminary study conducted to analyse its reliability and validity. Moreover, we present results from Factor Analysis performed to investigate the dimensionality of the instrument. Finally, we conclude by discussing the implications of our findings, along with limitations of the study and future work.
Enhancing the relevance of secretarial staff in the acquisition of office technology and management skills in the polytechnic system

It is a well-established fact that advances in technology has affected different professions with a view to meet global trends in higher productivity. Considering the highly competitive nature of today’s world of work driven by information and communications technology and coupled with the paucity of employment opportunities in Nigeria, secretarial staff need to value their positions and do whatever it takes to avoid becoming irrelevant and being replaced. Secretarial staff irrespective of their ranks and experiences, should as a matter of urgency acquire the necessary skills to enhance their relevance to accelerate their success in the workplace. Despite research studies on the impact of this technology, there has been a lack of empirical evidence on secretaries working patterns, interactions and communication, and specifically how office technology and management skills has changed working practices in the polytechnic systems. The sample of this study comprised secretaries and managers in Kaura Namodapolytechnic. The main findings revealed that sound knowledge of office technology and management skills increase the performance of the secretary for organizational growth. The findings also showed that the work of a secretary goes far beyond typing and taking down dictation. It has been noted that not everybody can perform the work of a secretary. It requires certain professional qualities such as diplomacy; tactfulness, initiative etc. and no executive can function well without a secretary.
The Relationships in the Law of Conservation of Mechanical Energy and the Ideal Gas Equation in Argon Molecules based on Moldy and Gnuplot

Science and technology have been progressing by leaps and bounds over the past few decades all over the world, resulting in the rapid development of advanced computer programs. Under such circumstances, we are able to prove plenty of theories in a new way with the help of different kinds of modern computer programs. For example, we are capable of investigating the relationship between kinetic energy and potential energy, as well as the connection between temperature and pressure, by applying computer programs to draw graphs. In this paper, moldy and gnuplot which are progressive computer programs are used to paint the graphs about the two relationships in the conservation law of mechanical energy and ideal gas equation respectively in argon gas with the help of the information obtained from the Internet. These two computer programs not only remove the burden of calculation, but also make the relationship clearer. In the end, this paper proves that the sum of kinetic energy and potential energy in argon molecules is invariable in a closed system under different temperatures. Pressure is also tested to have a positive linear association with temperature although the density changes when the argon gas is an ideal gas.
DEVELOPING STUDENT AGENCY THROUGH ONLINE DIDACTIC STRATEGIES IN HIGHER EDUCATION. SMALL GROUPS VERSUS LARGE GROUPS

During the COVID-19 pandemic, teachers and students were relocated in the digital space. In this study, we focus on two groups of students and two types of courses: electives on Romanian as a Foreign Language in Germany and seminars on the Grammatical Structure of the English Grammar in Romania. We analyse the role that student agency plays in the online teaching process. For this purpose, we applied a survey to analyse the effectiveness of the didactic strategies used with small groups in comparison with large groups. The findings of this study show that remote instruction at the university level during seminars/elective courses for small groups can be as efficient as on-site instruction for the learning process and, implicitly, for student agency development. Regarding large groups, the study shows that remote instruction can, at times, be even more efficient than face-to-face teaching, especially regarding student agency development.
Can the use of privacy enhancing technologies enable federated learning for health data applications in a Swedish regulatory context?

A recent report by the Swedish Authority for Privacy Protection (IMY) evaluates the potential of jointly training and exchangingmachine learningmodels between two healthcare providers. In relation to the privacy problems identified therein, this article explores the trade-off between utility and privacy when using privacyenhancing technologies (PETs) in combination with federated learning. Results are reported from numerical experiments with standard text-book machine learning models under both differential privacy (DP) and FullyHomomorphic Encryption (FHE). The results indicate that FHE is a promising approach for privacy-preserving federated learning, with the CKKS scheme being more favorable in terms of computational performance due to its support of SIMD operations and compact representation of encrypted vectors. The results for DP are more inconclusive. The article briefly discusses the current regulatory context and aspects that lawmakers may consider to enable an AI leap in Swedish healthcare while maintaining data protection.
Online Learning in University: Pros and Cons of Study

The purpose of this study was to determine the advantages and disadvantages of implementing online learning in the Economics Education study program. The research method used is a qualitative case study. Economics Education students and teachers were used as informants in this study. Data was collected using an online questionnaire (google-form), observation, and documents. Data analysis was carried out through three stages, namely data reduction, data presentation, and drawing conclusions. The research findings show that the disadvantages of online learning at Halu Oleo University outweigh the benefits. Several factors were found that both hindered and supported the success of online learning, including social factors from peers and parents, the completeness of technology learning tools, as well as the physical and psychological readiness of students and parents. So, it can be concluded that universities must pay attention to and prepare for all online learning needs. The results of this study have implications for lecturers that online learning has complex challenges and must know the needs, online learning facilities, readiness of human resources, and digital competence of teachers and students.
Small Group Discussion Method’s Effectivity for Improving the Students of Tenth-Grade Vocational High School 7 Semarang’s Speaking Ability

Many Indonesian students find it difficult to master English as a second language. One of the essential skills that must be mastered is speaking skill. People who like English must concerted effort to improve their English language skills. In teaching and learning, the teacher has a big responsibility. Teachers must master learning strategies to solve problems faced by the students and help them comprehend their speaking skills to achieve the goal of learning. This research uses experimental methodology to examine the effect of the small-group discussion method on students' ability to communicate verbally. The researchers applied a pre-experimental strategy, which included a pre-test before providing the chosen treatment. A post-test was given to complete the data collection after the treatments. The researchers used thirty-six students from SMK N 7 Semarang as a sample in this research. The research's findings indicates that the post-test score outperformed the pre-test score. On the other hand, at the significant level of 0,05, the t-score result is greater than the t-table. As a result, we can decide from the data analysis that the Alternative Hypothesis (Ha) is acceptable whereas the Null Hypothesis (Ho) is unacceptable. In accordance with the findings, conducting small group discussions as a teaching strategy significantly improves students' speaking abilities. As a result, it is suggested that English teachers offer this method as an attempt.
Design of Intelligent Wearable Devices for the Elderly Based on ARM

Aiming at the problem of not being able to take care of the elderly living alone at home in different places and not being able to timely understand their physical condition, this paper presents the design of a Intelligent wearable watch based on ARM. The device uses WiFi&BLE wireless communication module, oxygen sensor and heart rate, automatic voice recognition (ASR) technology module, gyroscope module, and RSSI technology. The device periodically detects some physiological parameters of the wearing site, and pushes them to the children's mobile phone through the WeChat public account periodically, so as to grasp the physical health status of parents remotely. The device automatically identifies the elderly who fall through the result algorithm of the gyroscope module, and timely reminds the children through the WeChat official account. The device has functions such as sleep monitoring, electronic fence, and fall detection, which facilitates the elderly and their families to pay attention to the health status of the elderly in a multidimensional way, and provides convenience for the elderly to live a high-quality life in their later years.
Learning Probabilistic Coordinate Fields for Robust Correspondences

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">when and where to trust</i> the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification.
الفكرة عن علم اللغة التربوي في تعليم اللغة الثانية

The Planning of language teaching has to be planned as good as possible to reach the purpose of that teaching. The Planning is not only about the theories but more than that it is about the other matters such as policy, organizing, these matters have role to help the teacher to succeed the program of foreign language teaching and how to implement an environment that can support the successful of language teaching and the capability of teacher about knowledge that will be taught. The concept of Educational Linguistics can describe about how a process or a plot of language teaching is arranged, the successful of language teaching is not only about language theories its self but also about the organizing and language teaching policy. &#x0D; This research is a library research which is included in the type of qualitative research, the data are collected from some resources that are devide into two parts, primary and secondary. The Primary resource is the book about educational linguistics written by Jos Daniel Parera and the book edited by Bernard Spolsky and Francis M. Hult with the title “The Hand Book Of Educational Linguistics”. The Secondary is the books about linguistics in general and the books about teaching and language teaching.
AI for fraud detection for Moroccan Customs Agencies (PoC)

AI for fraud detection for Moroccan Customs Agencies (PoC)Due to globalization and the increase in imports and exports, fraudulent activities such as tax evasion and smuggling have become more common.Manual inspections conducted by customs administrations can be costly, time-consuming, and prone to human error, making controls less reliable.To address this issue, the Moroccan Customs Administration is exploring the use of AI in its e-clearance system to detect various types of fraud such as misclassification of products, manipulation of origin country, and undervaluation of the imported goods.This study aims to examine the potential use of machine learning and deep learning algorithms to combat these types of fraud and reduce the assignment of customs agents by identifying suspicious transactions more effectively.The project is supported by the BACUDA project, a collaborative research project of the World Customs Organization (WCO), customs members, and data scientists.Its aim is to develop data analytics algorithms in open-source languages to enable all members to deploy them with their own data.The goal is to design features for each transaction, consisting of numerical and categorical characteristics, to allow algorithms to detect anomalies.This work is a proof-of-concept (PoC) highlighting the potential of AI to identify and prevent fraudulent activities in Moroccan customs administrations.Prof. Hind LAMHARHAR is a professor and a senior NLP and data engineer with a strong theoretical and practical background in both filed higher education and industry.She has working in many CS departments (INSEA: