Reviewerly/exHarmony huggingface dataset:
INTEGRATING REMOTE SENSING DATA WITH HYDROTHERMAL ALTERATION MAPPING AND GEOCHEMICAL CHARACTERISTICS OF PRECAMBRIAN ROCKS ACROSS MAMBILLA PLATEAU, NORTHEASTERN, NIGERIA

Mineral exploration must include the mapping of hydrothermally altered regions, which are typically connected to mineralization. This study presents a strategy for integrating remote sensing data with the geochemical characteristics of Precambrian rocks from the Mambilla Plateau in northern Nigeria. Tectonically, the lineaments were fractures with the following orientations: NE-SW, NW-SE, ENE-WSW, and N-S. The distinct spectral reflectance and absorption properties of remotely sensed Landsat 8 data in the visible, near-infrared, shortwave-infrared, and thermal infrared portions of the electromagnetic spectrum were exploited in various digital image processing approaches. Mapping hydrothermal alteration minerals was done effectively and efficiently using band ratios (red/blue, SWIR 2/NIR, SWIR 1/NIR), spectral band combinations (Kaufmann and Sabins ratios), and principal component analysis. Ferric, phyllic, propylitic, and argillic iron all underwent changes as a result of hydrothermal alteration. Regional metamorphism also resulted in important alteration processes such as epidotization, sericitization, muscovititization, kaolinitization, and chloritization. Geochemically, on average, the Al2O3, SiO2, Fe2O3, CaO, MgO, K2O, Na2O, Li2O content are 12.04 %; 54.83%; 4.76%, 7.16%, 3.02%, 10.22%, 4.53%, 1.35% while MnO, P2O5 and TiO2 are less than 1%. The average base metal composition revealed that 60% of them had positive anomalies, which indicate mineralization, and 40% had negative anomalies. About 50% of the samples had positive anomalies, which indicated strong potential for harbouring mineralization based on the makeup of trace elements. Geochemical studies revealed the presence of lithium, titanium, and silica oxides in substantial concentrations. The local mineralization is governed by structural factors.
Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.
One conflict, two public spheres, three national debates: comparing the value conflict over judicial independence in Europe across print and social media

Conflicts over the independence of judiciary as one of the European Union’s core democratic values is one indicator of democratic backsliding among its member states. Based on the Europeanization framework, we compare this conflict in German, Polish and Spanish print media and Twitter from 2019 to 2021. In the countries that are strongly affected by the value conflict, Poland and Spain, Europeanization is less evident. In contrast, the German discourse shows a high degree of Europeanization. We demonstrate that the print media is strongly elite-centric, while Twitter shows a higher actor visibility and inclusivity. However, we also identify important country differences.
Text Segmentation Via Processes that Count the Number of Different Words Forward and Backward

The paper is developing a new statistical approach to automatic partitioning of texts into parts belonging to different authors. It is based on the analysis of processes that counts the number of different words forward and backward. The theoretical study of the processes is based on the assumptions of an elementary probability model with a change point. We prove consistence of our statistical estimate of the point of concatenation in the case when the concatenated texts have different Zipf exponents. This method is being tested on the Brown corpus and also on newspaper texts in different languages. Testing shows a good estimate of the concatenation point. This method can be used in parallel with other text segmentation methods.
Utilising personas as a methodological approach to support prospective mathematics teachers’ adaptation and development of digital mathematics learning resources

Abstract Personas, initially originated in user experience research, are short and simplified representations of particular user groups, and this methodological approach has recently gained ground in educational research. This study aims to explore aspects of personas that may be beneficial for prospective mathematics teachers when they develop digital learning resources. To explore such aspects, we employed qualitative interviews, thinking-out-loud techniques, and jointly developed learning resources with prospective mathematics teachers, and analysed this diverse data with a combination of case study and grounded theory approaches. Consequently, we were able to identify the following essential aspects of using personas in our study: (A) personas as representatives of real people, (B) personas as planning &amp; feedback tools for material development, (C) professionalisation of prospective mathematics teachers (by using personas), (D) differentiation/individualisation for personas through digital learning resources, and (E) motivational elements of digital mathematics learning resources. Based on our results, we concluded that using personas could broaden prospective mathematics teachers’ views on student characteristics and demands that may enable teachers to facilitate the development of differentiated and individualised digital mathematics learning resources.
“DINO Vs. DINI” educational game to increase children's cognitive abilities—what are its level elements?

Games designed for children have the potential to serve as educational tools, incorporating learning components that promote engagement and facilitate the child's cognitive and developmental progress, tailored to their specific age group. The significance of Early Childhood Cognitive Abilities is in their association with everyday tasks and functions. The promotion of cognitive development in individuals with AUD necessitates the provision of many opportunities for cognitive stimulation. Children must attain fundamental competencies commensurate with their chronological age in cognitive development. The objective of developing this educational game is to assist early childhood education (PAUD) teachers and parents in delivering instruction through imaginative and inventive simulations. This study employs the Research and Development (R&amp;D) technique, utilizing a 4D model. The instructional game incorporates many level features that assess difficulty, accuracy, memory, and thinking abilities. This educational game encompasses four distinct learning modules tailored for early infant development. These modules include word completion exercises, counting activities, picture guessing tasks, color identification exercises, and competitive elements. The utilization of educational games is anticipated to facilitate the enhancement of children's cognitive capacities, thereby preparing them for the subsequent stages of their primary education.
Komparasi Metode Maut dan Moora dalam Pemilihan Sunscreen untuk Kulit Menggunakan Pembobotan ROC

The skin is the outer layer of the human body that has various important functions. However, high sun exposure in Indonesia can cause damage to the skin due to ultraviolet rays. The use of sunscreen becomes important in preventing sunburn and skin cancer. Women with combination skin types often have difficulty choosing the right sunscreen. This study applies the Multi Attribute Utility Theory (MAUT) and Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) methods with the weighting of the Centroid Rank Order (ROC) method and the level of accuracy of MSE aims to produce decisions in choosing the right sunscreen for combination skin. At the methodology stage, data on criteria, sub-criteria, and alternatives are collected through observation and interviews. The criteria consist of 6 namely Benefit, Composition, Price, Vitamins, Side Effects, and Size. The ROC is used for weighting, while the MAUT and MOORA methods are used in the assessment and comparison of alternatives and MSE is used for the level of accuracy. The discoveries from this study hold the possibility to offer recommendations for choosing the best sunscreen for combination skin, namely A7 with the brand sunscreen Somethinc Comfort Correct for the MAUT method is 0.7134 and the MOORA method is 0.102. The results of the MSE calculation obtained a deviation value, namely the MOORA method with a value of 215.0091 better than the MAUT method of 207.4922. So that the MOORA method is the best method in choosing sunscreen for combination skin and aims to help people who are still having difficulty choosing the right sunscreen, so as to avoid mistakes in choosing inappropriate sunscreens that can have a negative impact on the skin
Interns: Mentoring and Counseling on the Software Development Process

The Digital Transformation Services Internship Program at PT Arkatama Multi Solusindo aims to provide opportunities for intern students to develop themselves through direct experience in real projects. With a comprehensive learning approach, interns are given the flexibility to choose positions according to their interests, such as UI/UX designer, Web Developer, and others. This program not only focuses on developing technical skills but also emphasises moral education and professional ethics. Intern students will be involved in prototyping and follow the development process from planning to implementation. The results of their work are documented through logbooks and final reports, building a valuable portfolio. The internship is carried out on-site according to health protocols at PT Arkatama Multi Solusindo, which focuses on digital transformation in various sectors. Services provided include application development, IT Training, and Digital Marketing Services.
The impact of monetary policy instruments on sustainable development

In this research, the effect of monetary policy on five sustainable development indicators prepared by the United Nations was investigated in Iraq. For this purpose, variables of interest rate, exchange rate and volume of money in circulation have been used as monetary policy tools and the indicators have been used such as Proportion of the population living below the international poverty line (1.1.1), Proportion of population using safely managed drinking water (6.1.1.), Change in water-use efficiency (6.4.1), Renewable energy (7.2.1), share in the total final energy consumption, and Annual growth rate of real GDP per capita (8.1.1.). For this purpose, five regressions were estimated using the Two Stage Least Squares (2SLS) method. The results show that the exchange rate has a positive effect on indicators of poverty, efficient management of water resources, and economic growth per capita, and a negative effect on access to safe drinking water. Among other monetary policy instruments, only currency in circulationhas a positive effect on poverty reduction, and in other cases it has no significant effect.
Media Pembelajaran Multisensoris Mengunakan Flashcards Berbasis Augmented Reality untuk Anak Disleksia

Dyslexic children have difficulty recognizing letters with similar shapes such as b and d, w and m, n and u, p and q as a result they often misspell words and have poor reading and writing skills. Multisensory learning is an effective method of learning to read in dyslexic children. The traditional teaching methodology in schools today combines many students in one class, making it quite difficult for a teacher to apply remedial and multisensory methods to dyslexic students so that multimedia applications have an important role in the learning activities of dyslexic students. In this study the researcher tried to develop learning media using the Luther method, intended for Indonesian dyslexic children in which to optimize multisensory methods using augmented reality-based flashcards. Learning media has passed product testing and testing by experts. The test results showed that the flashcards have been designed have a marker rating of 3-5 (scale 1-5). The higher the rating, the faster the accuracy and speed of the flashcard scanning process. The test data showed that flashcards with a rating of 4-5 require a scan time of less than one second, while flashcards with a rating of 3 required a scan time of 1-2 seconds. Overall, the learning media developed can function properly, and are useful for supporting multisensory learning in dyslexic children.
Pembelajaran Membaca Puisi dengan Menggunakan Metode Demonstrasi di Madrasah Tsanawiyah

AbstractThis study aims to describe the effectiveness of the demonstration method in learning to read poetry. This research does not carry out control variables so it is also called non-experimental research. This study only describes the object as it is. This study uses learning library techniques and test techniques that aim to obtain data. This technique is used to obtain data on the initial and final test scores for learning to read poetry by using the demonstration method. Based on the results of the study, it was found that the use of the demonstration method in learning to read poetry in class VII MTs PUI Maja in the 2013/2014 academic year could effectively improve students' abilities. This is proven based on the calculation using the product moment correlation formula above, it is obtained r = 0.88 or 0.88% and based on the guidelines for providing interpretation the correlation coefficient is included in the high correlation.Keywords: Method, Reading Poetry, DemonstrationAbstrakPenelitian ini bertujuan untuk mendeskripsikan kefektifan metode demonstrasi pada pembelajaran membaca puisi. Penelitian ini tidak melakukan control variable sehingga disebut juga penelitian non-eksperimen. Penelitian ini hanya menggambarkan objek apa adanya. Penelitian ini menggunakan teknik kepustakaan pembelajaran dan teknik tes yang bertujuan untuk memeperoleh data. Teknik ini digunakan untuk memperoleh data nilai tes awal dan tes akhir pembelajaran membaca puisi dengan menggunakan metode demonstrasi. Berdasarkan hasil penelitian, diperoleh bahwa penggunaan metode demonstrasi dalam pembelajaran membaca puisi di kelas VII MTs PUI Maja Tahun Ajar 2013/2014 efektif dapat meningkatkan kemampuan siswa. Hal tersebut terbukti berdasarkan penghitungan menggunakan rumus korelasi product moment di atas diperoleh r = 0,88 atau 0,88% dan berdasarkan pedoman pemberian interpretasi koefisien korelasi termasuk pada korelasi tinggi.Kata kunci: Metode, Membaca Puisi, Demonstrasi
Feature fusion network based on few-shot fine-grained classification

The objective of few-shot fine-grained learning is to identify subclasses within a primary class using a limited number of labeled samples. However, many current methodologies rely on the metric of singular feature, which is either global or local. In fine-grained image classification tasks, where the inter-class distance is small and the intra-class distance is big, relying on a singular similarity measurement can lead to the omission of either inter-class or intra-class information. We delve into inter-class information through global measures and tap into intra-class information via local measures. In this study, we introduce the Feature Fusion Similarity Network (FFSNet). This model employs global measures to accentuate the differences between classes, while utilizing local measures to consolidate intra-class data. Such an approach enables the model to learn features characterized by enlarge inter-class distances and reduce intra-class distances, even with a limited dataset of fine-grained images. Consequently, this greatly enhances the model's generalization capabilities. Our experimental results demonstrated that the proposed paradigm stands its ground against state-of-the-art models across multiple established fine-grained image benchmark datasets.
A human-centered safe robot reinforcement learning framework with interactive behaviors

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is a crucial step toward achieving human-robot coexistence. In this paper, we envision a human-centered SRRL framework consisting of three stages: safe exploration, safety value alignment, and safe collaboration. We examine the research gaps in these areas and propose to leverage interactive behaviors for SRRL. Interactive behaviors enable bi-directional information transfer between humans and robots, such as conversational robot ChatGPT. We argue that interactive behaviors need further attention from the SRRL community. We discuss four open challenges related to the robustness, efficiency, transparency, and adaptability of SRRL with interactive behaviors.
A universal algorithm for defect-free atomic array with arbitrary periodic geometries [Invited]

The defect-free neutral atom array has emerged as an ideal platform to investigate complex many-body physics of interacting quantum particles, offering the opportunities for quantum simulation and quantum-enhanced metrology. To fast build a large-scale quantum system, we design a sorting-atom algorithm with maximum parallelisms. Compared with previous protocols, our method saves the rearrangement time by sorting row-by-row and is also universal to arbitrary periodic patterns with no need to change the hardware. We present the generation of a defect-free square and other periodic geometries and demonstrate the potential to scale up a defect-free array to 2500 atoms with only about 180 steps of rearrangement.
SAS Method: Creating Equity and Adaptivity for Students' Linguistic Skills

The aim of this research was to describe the application of the SAS (Synthetic Structural Analytical) method as an effort for equality and adaptivity towards the linguistic skills of early age students. This research used the type of Classroom Action Research. The subjects of this research were class B1 students at TPA (20 students). Data collection techniques use observation, interviews and (closed) tests. The data analysis technique used is analysis of the percentage test results for students' reading skill categories based on the results obtained on observation sheets taken during the learning process. The results prove that the SAS method is able to develop students' reading skills so that they are effective and successful in achieving quality and adaptivity later at the elementary school level. The increase in the percentage of students' reading skills was obtained from the five aspects (sensory, perceptual, schemata, thinking and affective) which encouraged them to develop their reading skills in a structured and synthetic analysis. This research specifically described the success of the SAS method which can not only be a technique for improving students' reading skills, but can also make
What Influences People’s Adoption of Cognitive Cybersecurity?

Artificial intelligence (AI) and big data, among other new technologies, have attracted the attention and interest of practitioners and researchers in cognitive cybersecurity regarding the use of AI technologies to protect digital as well as analogue systems. This mixed-method study explores the main determinants of cognitive cybersecurity adoption at the individual level. We propose an integrated model with unified theory of acceptance and use of technology model in combination with trust, word-of-mouth, information security awareness, and habit. We empirically examine the hypotheses using a survey of 267 people from southern Europe. The results obtained from the use of structural equation modelling show that performance expectancy, effort expectancy, and word-of-mouth affect behavioural intention. The behavioural intention and word-of-mouth influences use behaviour. The results were triangulated with field interviews, which supported the findings. This study offers a valuable contribution to software-selling companies that are at the moment, or will in the future be, interested and/or involved in developing cognitive cybersecurity software to be used at an individual level.
Uncovering the audience perspective: A qualitative analysis of experiences and evaluations of two immersive journalism productions in the Netherlands

There has been limited adoption of Immersive Journalism (IJ) by the audience; simultaneously, the audience’s perspective is rarely considered in the production and research of IJ. At this point, however, it is crucial to incorporate an audience perspective to identify potentially unintended effects of IJ and improve on the innovation of IJ. This study investigates the audience’s experience and evaluation of IJ by qualitatively analyzing their thoughts after viewing two IJ cases. Our results indicate that the audience may pick up on intended effects, such as a sense of presence and an intense emotional experience, but some also express unease towards these effects. Furthermore, the audience struggles to comprehend this study’s two immersive journalistic cases as part of the journalistic genre. These findings provide insight into the gap between the initial hype and the current reality of IJ and provide the basis for propositions for future IJ productions.
Efficient Core Maintenance in Large Bipartite Graphs

As an important cohesive subgraph model in bipartite graphs, the (α, β)-core (a.k.a. bi-core) has found a wide spectrum of real-world applications, such as product recommendation, fraudster detection, and community search. In these applications, the bipartite graphs are often large and dynamic, where vertices and edges are inserted and deleted frequently, so it is costly to recompute (α, β)-cores from scratch when the graph has changed. Recently, a few works have attempted to study how to maintain (α, β)-cores in the dynamic bipartite graph, but their performance is still far from perfect, due to the huge size of graphs and their frequent changes. To alleviate this issue, in this paper we present efficient (α, β)-core maintenance algorithms over bipartite graphs. We first introduce a novel concept, called bi-core numbers, for the vertices of bipartite graphs. Based on this concept, we theoretically analyze the effect of inserting and deleting edges on the changes of vertices' bi-core numbers, which can be further used to narrow down the scope of the updates, thereby reducing the computational redundancy. We then propose efficient (α, β)-core maintenance algorithms for handling the edge insertion and edge deletion respectively, by exploiting the above theoretical analysis results. Finally, extensive experimental evaluations are performed on both real and synthetic datasets, and the results show that our proposed algorithms are up to two orders of magnitude faster than the state-of-the-art approaches.