Reviewerly/exHarmony huggingface dataset:
Detecting speech recognition errors using topic information and BERT

Detecting errors in speech recognition is important because these errors negatively affect the accuracy of downstream tasks. To detect recognition errors, we focus on context, label sequence, and topics. Words in a sentence contain the context. Context information is useful in detecting errors from speech recognition because the errors must be outside the scope of the context. Furthermore, recognition error detection is a sequential problem, namely a sequential labeling task. Therefore, handling a label sequence is useful for error detection. In addition, a target speech contains topics, such as a talk about music, sports, and so on. A word that does not match the topic is considered an error. Therefore, the topic information is also useful. In this paper, we propose a method based on BERT, CRF, and LDA from the viewpoints BERT is used for context recognition. Conditional Random Fields (CRF) are used for the sequence labeling task. Latent Dirichlet Allocation (LDA) is to capture topic information. In this study, we compared several combinations of them: BERT only, BERT-CRF, and BERT-LDA-CRF models. We evaluate the models with the Corpus of Spontaneous Japanese (CSJ) corpus. The experimental result shows the effectiveness of the integration of CRF and LDA into BERT.
Design for Global Challenges

The current emergencies such as climate change and sustainable development, are outlining the responsibility we have as researchers and designers to transform disruptive technologies into action/reaction tools able to address and overcome the nowadays challenges. From this perspective, as designers, we indeed believe that the key to change lies in human behavior, and thus precisely in this direction design can impact using disruptive technologies in raising awareness by resulting into behavioral change. Specifically, this paper aims to encompass how advanced technologies such as Data Visualization and Immersive Realities can cooperate in order to activate proactive behaviors in contemporary society through a careful analysis of opportunities and limitations. On the one hand, Data Visualization has always been used as a tool for analyzing and communicating emergencies (Snow, Nightingale, etc.), on the other hand, immersive realities are more recently beginning to explore this field. It is indeed at the meeting point between technology and Human Interface that the biggest gaps emerge, sometimes making the deployment of these technologies fail, due to sensory, sociological, psychological, and cognitive limitations. Have we ever wondered, for example, whether the rules of Gestalt and Bertin's theories, belonging to the first half of the 1900s, are sufficient today in making effective Data Visualization? Are we relying too much on the "astonishing" effect of immersive realities, paying more attention to their use rather than to the conveyance of the content? This paper attempts to critically analyze – in a multidisciplinary way and through a historical analysis, case studies, and personal experimentation – the use of disruptive technologies, such as XR and Data Visualization, trying to clarify their limits, potentialities, and plausible common application fields. The objective of the study is in fact related to the determination of possible theoretical and practical approaches for the development of validation tests regarding the use of disruptive technologies and their relationship with the Human Interface.
Genesis of Sulfide Mineralization in the Gallala Area Kurdistan Region, Iraq: Constraints from Major and Trace Elements of Sulfide Ore Mineralization and their Host Rocks

This article presents and interprets new geochemical data from the Gallala sulfide mineralization, situated in the Choman District of the Kurdistan Region of Iraq close to the Iraq and Iranian border. The host rocks belong to the Walash-Naopurdan Group, which developed in the Paleocene-Eocene age in the Zagros Suture Zone. The ore mineral assemblage of the Gallala area consists of pyrite, chalcopyrite, sphalerite, and magnetite. The mineral chemistry and geochemical analysis indicate that there is a significant variation in concentrations of pyrite, chalcopyrite, sphalerite, and magnetite from the Gallala mineralization. The trace element characteristics of the sulfide mineralization and their host rocks indicate that the ore-forming materials were derived from hydrothermal fluids being interacted with the igneous rocks. The trace element signatures and REE distributions of the studied samples, tectonically lead to suggest island-arc and back-arc environments in the Gallala area. Additionally, the REE patterns of the Gallala sulfides indicate the ore-forming fluids are related to arc-related hydrothermal systems. The integration of geochemical features along with the chemistry of ore mineralization such as the Selenium/Sulfur ratio of pyrite, Co/Ni ratios from both pyrite and chalcopyrite, the Zn/Cd values from the sphalerite, and the petrogenetic discriminate diagrams of magnetite reveal that the genesis of the sulfide mineralization can be attributed to volcanogenic to hydrothermal ore-formations with high to medium temperatures origin.
A First Look at Shill Looping in NFT Ecosystem

Initially designed to represent ownership of various assets, Non-Fungible Tokens (NFTs) have emerged as a new tool in the blockchain domain for investment and trading. The NFT markets are rapidly budding with significant growth in trading volumes over the last few years. While the NFT ecosystem is continuously evolving, users are exploring astute trading practices to gain financial profits. In this paper, we uncover shill looping, a novel NFT trade practice that NFT owners can exploit to artificially inflate the price of an NFT token. We investigate shill looping and its primary effects in a multi-billion dollar NFT collection called BAYC, showing that approximately 50% of these NFTs exhibit shill looping. Our empirical analysis shows that shill looping significantly boosts the average NFT values by over 45% in the best case. Our initial results highlight the severeness of the shill looping phenomenon and open a new research direction for further exnloration.
A Transfer Learning Approach to Cross-Domain Author Profiling

Author profiling is the process of analysing text to determine one or more identifying characteristics of the author, mostly used to determine key demographic information. This type of classification problem is ideally suited to machine learning approaches. In this study, a new transfer learning approach is introduced using a pretrained XLNet language model which was then fine-tuned to the specific author profiling task. Informed by previous literature, a Support Vector Machine, Feed-Forward Neural Network, and Convolution Neural Network were also developed for comparison. These algorithms were used to predict gender and age group on a single training and testing domain. As a model that works across multiple domains is desirable, each model was also tested on two domains which were independent of the training domain. The results demonstrated that the transfer learning model is superior to the other methods used for comparison in this study. Although applying the transfer learning model to the cross-domain context decreased its performance, it was still able to achieve a higher degree of accuracy on one testing domain than the Support Vector Machine which was trained and tested on that same domain. In addition, some interesting results emerged regarding the transfer of hyperparameter performance between tasks that share a common factor, be that classification task or training domain.
Modifikasi Algoritma Edmonds Karp untuk Menentukan Aliran Maksimum Pada Jaringan Distribusi Air PDAM (Studi Kasus Jaringan Telaga Sari PDAM Giri Menang Mataram)

Clean water is the main and basic need for humans which is of concern to the government. Distribution network system is a very important part to delivering water to all consumers. The lack of water discharge distribution in several areas, especially at the end of the pipeline service, is cause by not optimal water distribution, the flow rate of sorce and leak in pipeline effect. This research has to analyze the optimal network model and determine the maximum flow rate from the PDAM pipeline using modified Edmonds Karp algorithm. Modified Edmonds Karp algorithm is a method for calculating maximum flow of a network. Based on analysis of modified Edmonds Karp algorithm there is a less efficient us of pipe in PDAM network and result of maximum flow from the network is 202,30 liter/second. This means it can be adding flow discharge to the water distribution pipe by PDAM for expedite the flow to consumer with the addition of flow should not exceed 202,30 liter/second.
THE EFFECT OF REWARD AND PUNISHMENT METHODS ON LEARNING OUTCOMES OF IPS IN CLASS IV STUDENTS

&lt;p&gt;&lt;em&gt;This study aims to: 1) determine differences in social studies learning outcomes between classes given the reward and punishment method and conventional classes in fourth grade students at SDN 23 Singkawang; 2) find out how much influence the reward and punishment method has on social studies learning outcomes in fourth grade students at SDN 23 Singkawang. The type of research used is quasi- experimental quantitative research (quasi experimental design) with a Nonequivalent control group design with the form of experimental class and control class. The sampling technique used purposive sampling was selected for the experimental class, namely class IV B and control class, class IV A. The experimental class was treated using the reward and punishment method, while the control class was not treated using conventional learning. The data analysis technique used in this study is the two sample t test and the effect size formula. The results showed (1) there was an influence between classes using the reward and punishment method and classes using conventional learning, seen from the calculation of the post-test learning outcomes of the experimental class and the control class using the two- sample T test showed that tcount = 2.368 &amp;gt; ttable = 2.0048 with a significance level of 5%, meaning that Ha is accepted and Ho is accepted rejected; (2) the reward and punishment learning method has a high effect on student social studies learning outcomes by using the effect size formula with a value of 1.104.&lt;/em&gt;&lt;/p&gt;
Speed Limit of Efficient Cavity-Mediated Adiabatic Transfer

Cavity-mediated adiabatic transfer (CMAT) is a robust way to perform a two-qubit gate between trapped atoms inside an optical cavity. In the previous study by Goto and Ichimura [H. Goto and K. Ichimura, Phys. Rev. A 77, 013816 (2008).], the upper bound of success probability of CMAT was shown where the operation is adiabatically slow. For practical applications, however, it is crucial to operate CMAT as fast as possible without sacrificing the success probability. In this paper, we investigate the operational speed limit of CMAT conditioned on the success probability being close to the upper bound. In CMAT both the adiabatic condition and the decay of atoms and cavity modes limit the operational speed. We show which of these two conditions more severely limits the operational speed in each cavity-QED parameter region, and find that the maximal operational speed is achieved when the influence of cavity decay is dominant compared to spontaneous emission.
Exploring Factors Shaping Mobile Application Usage in Rural Uttarakhand: A Study in Selected Villages of Chamoli, Pauri, Rudraprayag, Tehri, and Uttarkashi Districts

This research aimed to investigate the impact of diverse factors on the usage behavior of mobile applications among users in the rural regions of Uttarakhand specifically in villages situated in Chamoli, Pauri Garhwal, Rudraprayag, Tehri Garhwal, and Uttarkashi districts. The primary objective was to ascertain whether factors such as the Ease of Use of applications, Privacy &amp; Security considerations, Satisfaction levels, Digital Literacy levels, and Internet Accessibility collectively influence the usage behavior of mobile application users. To conduct the study, a descriptive research design was employed, and primary data was collected from rural areas in Uttarakhand. The influence of various factors was analyzed using the ANOVA test. The findings of the study indicate that all the aforementioned factors significantly impact the usage behavior of mobile applications in these rural areas. Additionally, the study suggests a widespread increase in the future adoption of mobile applications in these regions.
Exploring Transformative Leadership Approaches in Modern Educational Institutions

This study examines transformative leadership within Philippine educational settings to discern its impact on student outcomes, staff development, and institutional effectiveness. A descriptive qualitative analysis involving diverse stakeholders revealed that transformative leadership diverges significantly from traditional management, emphasizing innovation, empathy, and adaptability. It fosters a culture where staff confidence and autonomy lead to professional growth and improved educational outcomes. In addition, the findings suggest that such leadership is essential for educational institutions navigating the complexities of modern education, including technological advancements and societal changes. Effective communication and systematic evaluation were identified as key for transformative leaders to measure and enhance student success, despite facing resistance to change. The study concludes that transformative leadership will become increasingly central in education, driving institutions towards inclusivity and a forward-thinking ethos. It was recommended that Philippine educational institutions adopt transformative leadership to bridge disparities, enhance quality, and prepare for future educational demands. The approach is particularly beneficial for public schools, higher education, TVET institutions, private educational providers, policymakers, and community learning centers, promising to elevate the quality of education in a rapidly evolving global landscape.
Computational Thinking: The Essential Skill for being Successful in Knowledge Science Research

The VUCA world concept was established in 2016 as the new challenge universe in the 21st century. Humans live in Society 5.0 and the VUCA world simultaneously. The digital word has been a noisy word since then. There are a lot of requisite skills to be a survival kit for this kind of era. The VUCA world's affection is spreading in the way of thinking and creating innovation, especially in the research domain. As a newcomer, Knowledge Science should state the requisite skills for its researchers to conduct their research successfully. Many researchers offered computational thinking as a candidate for an essential skill to satisfy the effect of the VUCA world. This study was focused on conducting a descriptive analysis method based on several literature reviews for mapping how computational thinking can serve as a best practice for Knowledge Science research. This study successfully revealed the connection between Computational Thinking.
Optimizing Convolutional Neural Network Architecture

Convolutional Neural Networks (CNN) are widely used to face challenging tasks like speech recognition, natural language processing or computer vision. As CNN architectures get larger and more complex, their computational requirements increase, incurring significant energetic costs and challenging their deployment on resource-restricted devices. In this paper, we propose Optimizing Convolutional Neural Network Architecture (OCNNA), a novel CNN optimization and construction method based on pruning and knowledge distillation designed to establish the importance of convolutional layers. The proposal has been evaluated though a thorough empirical study including the best known datasets (CIFAR-10, CIFAR-100 and Imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40 and MobileNet), setting Accuracy Drop and Remaining Parameters Ratio as objective metrics to compare the performance of OCNNA against the other state-of-art approaches. Our method has been compared with more than 20 convolutional neural network simplification algorithms obtaining outstanding results. As a result, OCNNA is a competitive CNN constructing method which could ease the deployment of neural networks into IoT or resource-limited devices.
Characterizing Intercultural Competence among Cybersecurity Majors

Intercultural competence has taken on renewed importance due to the increasingly diverse nature of work environments. The prevalence of virtual and remote teams in recent years has also contributed to the growing need for an interculturally trained workforce. There is a demand for graduates to be able to effectively work with those from cultures and backgrounds different from their own. Several higher education institutions in the United States have responded to this by first assessing their students using various assessments to determine their intercultural competence. Universities have further incorporated concepts of intercultural competence into their regular curriculum of study with the goal of equipping students with the skills to be successful in intercultural environments. This study is situated in a STEM classroom at the Polytechnic Institute of a large midwestern university. This study also attempts to address the dearth of literature pertaining to intercultural development in STEM fields with a specific focus on the cybersecurity discipline. This study is guided by the research question, how can intercultural competence be characterized for students enrolled in a first-year introductory cybersecurity course? The students enrolled in this course were provided trainings using portable intercultural learning modules (PIMs) focused on productive conflict management as well as the cultural influences on the intent and impact of communication practices. A rubric adapted from the behavioral rubric of Intercultural Skills Development was used to score 50 student reflections following the aforementioned intercultural training. The data was analyzed using a mixed-methods approach. Descriptive statistical analyses were conducted to understand the overall intercultural gains for all students. Furthermore, hierarchical clustering was used to analyze the data. The clustering algorithm helped to divide the students into two clusters, with one displaying high intercultural competence and the other exhibiting low intercultural competence. In addition, patterns of intercultural competence were disclosed in the form of sample quotes from the student reflections. Overall, the results are encouraging as they add to the body of literature at the intersection of cybersecurity education and intercultural competence.
EFFORTS TO INCREASE READING INTEREST IN ELEMENTARY SCHOOLS

This study aims to find alternatives to increase students' interest in reading, especially among elementary schools. Interest in reading is a big influence for students. Efforts to increase elementary school students' interest in reading are a shared responsibility, between the students themselves, teachers and parents. However, the low reading interest of students in elementary schools is one of the obstacles, the lack of desire and willingness from the students themselves, the teacher also does not require students to read books when learning is being taught to students. In an effort to increase elementary school students' interest in reading, students should be given support so that interest in reading emerges from students and is introduced to reading material so that students are accustomed to reading, therefore the reading habits of students when they are in elementary school will foster a high interest in reading. students grow up. This research was conducted by formulating a research problem looking for references from relevant and similar journals to analyze the data, journals were collected by searching the internet through Google Chendikia and/or Google Scholar. The data obtained were analyzed and compared with this research method using a descriptive research type with a quantitative approach. The results of the analysis can increase elementary school students' interest in reading.
Pemilihan Bibit Padi yang Tepat untuk Musim Hujan Menggunakan Algoritma MOORA dalam Sistem Pendukung Keputusan

The optimal selection of rice seedlings is a key factor in enhancing rice farming productivity during the rainy season. To achieve this goal, this research utilizes a Decision Support System (DSS) based on the MOORA Algorithm (Multi-Objective Optimization by Ratio Analysis). This study explores the selection of the best rice seedlings for the rainy season using the MOORA Algorithm. Evaluation criteria include plant height, harvesting time, tolerance to waterlogging, resistance to pests, disease resistance, and average yield per hectare. The research results identify Inpari 30 Ciherang Sub-1 (BP07) as the top choice for the rainy season with a Yi (max) value of 0.2998. This is followed by Inpara 4 (BP03) in second place with a Yi (max) value of 0.2913, and Ciherang (BP02) in third place with a Yi (max) value of 0.2849. We hope that the research findings will provide a positive contribution to improving agricultural productivity in the future and assisting farmers in better facing the challenges of the rainy season.
A look into the Phenomenon of Multi-religious Culture in Korea

[Objective] This paper intends to introduce how Korea's multi-religious culture coexists, focusing on several examples. In this way, we could look the clues that can solve the conflict and friction inter religions in Korean society. And as a solution, I would like to raise the need for religious education to respect differences between religions.&#x0D; [Contents] Korean society is a multi-religious coexistence society that is difficult to see in the world religious history. According to the 2021 Korea Research Survey, Protestants accounted for 20% of the total population, 17% Buddhists, and 11% Catholics, and 50% of the total population has no religion. This phenomenon is also common among Korean family members. In this way, Korea can be said to be a model for a modern multi-religious society. The reason for this phenomenon can be explained as follows. First, the religious composition of Koreans, second, those religions are undergoing a process of secularization, and third, freedom of religion. However, even in Korean society, there are serious conflicts and strife between religions. And this conflict also exists among family members. Then, how can Korean society continue to have an order of religious coexistence?&#x0D; [Conclusions] I believe that religions in our society should continue to have dialogue to understand each other. In addition, I believe that the order of religious coexistence can be solved by teaching religious education in public education. This is to enable the next generation to have the eyes of religious literacy. Finding such an order is very important for atheists who make up 50% of the Korean population and for finding the key to resolving religious conflicts in the global society.
Industrial Examples of Graph Applications : Plenary Talk

Graph-based computational methods have been used in various contexts to tackle the inherent complexity of large, relationship-oriented datasets. They promise the ability to handle the semantics of the data, as opposed to purely statistical methods. From cybersecurity risk evaluation to automated driving, and knowledge management, we present a few sample applications where graph-based methods are being applied to real-life engineering challenges in an industrial context. These very diverse applications show the power of graphs to represent and process complex information effectively.