Reviewerly/exHarmony huggingface dataset:
UNDERSTANDING INTER-CULTURAL COMMUNICATION: A STUDY CASE IN WORKPLACE

This research intends to find out the obstacles of ethnicity and language in the communication of the import-export company PT. Jalak Kargo Logistik. This research focuses on the effect of ethnic and cultural diversity on the effectiveness of communication in the company. Therefore, it is necessary to study using several theories such as Intercultural communication by Wiggin, Cross-cultural communication by Mitra, Language Barrier by Bowen, Cultural Adaptation Workplace by Haninggan, code switching by Shana Poplack, and code mixing by Holmes. The results of this research show that the use of code switching and code mixing have been used in intercultural communication to solve the language barriers and it has effectively reduced the cultural gap between individuals in the working place.
Translation Errors from Bahasa Indonesia into English

The purposes of this research were to find out some translation errors from Bahasa Indonesia into English and to describe the causes of translation errors. This was a descriptive qualitative research that was conducted at IAIN Langsa. The object of the research was the students of English Department, IAIN Langsa who had taken the subject of Translation III. The total number of the object was 12 students. The researchers used documentation and questionnaire as the research instruments. The documentation was taken from the result of translation exam given by the lecturer. The documentation was used to find out the translation errors made by the students. The researchers also gave a closed questionnaire that consisted of 10 questions to the students. As the result, the researchers found that there were some errors in translating texts from Bahasa Indonesia into English. The causes of errors made by the students were that the students were difficult to translate texts from Bahasa Indonesia into English; most students did not know how to differentiate between literal and idiomatic translation; most students were not familiar with translation methods; most students did not master English vocabularies well; and the students felt difficult to choose the appropriate meaning of words that have various meaning.
Model Development of Children’s Language Skills Based on Digital Literacy

This research generally aims to develop a model of children’s language skills based on digital literacy in Pondok Benowo Indah RT 03 RW 08 Babat Jerawat, Pakal Surabaya. First, to determine the process or stages of learning language skills using narrative stories or fairy tales from a YouTube application that is watched by them and is accompanied by their parents. The two processes or stages of developing language skills are by retelling stories or fairy tales that they see with their parents and writing moral messages in the stories or fairy tales. The method used in this study is quantitative-qualitative (mix method). The data were analyzed using content analysis techniques. The data will be analyzed through the following steps: 1) classifying the data, 2) displaying the data, 3) interpreting the data, and 4) drawing a conclusion. The study intends to create and develop a role of language skills for the children who mostly like their devices compared to playing together with their peers outside the home. For parents, this model of language skills can later be applied at home when they are together and interact with their children. For children, this is a diversion from the gadgets they often hold when they are at home.
Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.
AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE

Recently, introspective models like IntroVAE and S-IntroVAE have excelled in image generation and reconstruction tasks. The principal characteristic of introspective models is the adversarial learning of VAE, where the encoder attempts to distinguish between the real and the fake (i.e., synthesized) images. However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images. In this paper, we propose a new variation of IntroVAE called Adversarial Similarity Distance Introspective Variational Autoencoder (AS-IntroVAE). We theoretically analyze the vanishing gradient problem and construct a new Adversarial Similarity Distance (AS-Distance) using the 2-Wasserstein distance and the kernel trick. With weight annealing on AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and high-quality images. The posterior collapse problem is addressed by making per-batch attempts to transform the image so that it better fits the prior distribution in the latent space. Compared with the per-image approach, this strategy fosters more diverse distributions in the latent space, allowing our model to produce images of great diversity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image generation and reconstruction tasks.
Peran Inputer Indikator Kinerja Utama (Iku) Dalam Pengelolaan Data Kinerja Direktorat Perencanaan Strategis Dan Manajemen Kinerja Badan Pemeriksa Keuangan

Performance data management is carried out by recording, processing, and reporting performance data. The measurement of performance achievement at BPK is supported by the role of the Key Performance Indicators (KPI) input. Where the KPI input does not meet the level of reliability. The purpose of this study was to find out what factors caused the role of KPI inputers to not fully meet the level of reliability (timeliness of data input, completeness of supporting data and accuracy of supporting data) in the ideal SIMAK/PRISMA data input; and develop strategies to increase the role of KPI inputers in order to increase reliability. This study uses a qualitative research method with a descriptive approach using Soerjono Soekanto's active role theory. Sources of data obtained from primary data and secondary data. The analysis technique uses SWOT analysis. Data collection techniques through interviews, observations and documentation carried out at the Directorate of Strategic Planning and Performance Management. Resource persons as informants in this study are KPI inputers. The results showed that the factors that caused the role of the KPI inputer to not fully meet the ideal level of reliability in inputting SIMAK/PRISMA data were the KPI inputer in carrying out the role of prioritizing the main task, where being a KPI inputer was an additional task; availability of data from parties other than KPI inputers who have not provided data by the time limit; there is limited knowledge of the inputer on the use of the application; the data period required is different from the input period; the existence of human error, the inputer mistakenly saw the numbers and typed them; the data to be input takes time in a tiered review, the work process is still ongoing so that the required data does not yet exist; data/document access restrictions. There are six strategies that can be used to increase the role of KPI inputers, namely maintaining social relations/interrelationships on an ongoing basis in order to cooperate with other parties regarding data input; maximizing the facilities and infrastructure to support the task of inputting data; propose a re-evaluation of data access restrictions; propose to take part in related training for unskilled KPI inputers; proactive in collecting data from various parties.
A Combination of Host Overloading Detection and Virtual Machine Selection in Cloud Server Consolidation based on Learning Method

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection. Thereby reducing the generation of SLAV, ensuring the performance and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption.
A Review Of Clustering Methods Based on Artificial Intelligent Techniques

Due to the development in various areas of life, the development of the Internet, and the presence of many datasets, and in order to obtain useful information from the rapidly increasing volumes of digital data, there must be theories and computational tools to help humans extract the useful information they need from this data.Large data is collected from many different services and resources.Clustering is one of the most basic and well-known methods of data mining and extraction and obtaining useful information.The technique of recognizing natural groups or clusters within several datasets based on some measure of similarity is known as data clustering.Many researchers have introduced and developed many clustering algorithms based on the different methods of artificial intelligence techniques.Finding the right algorithms greatly helps in organizing information and extracting the correct answer from different database queries.This paper provides an overview of the different clustering methods using artificial intelligence and finding the appropriate clustering algorithm to process different data sets.We highlight the best-performing clustering algorithm that gives effective and correct clustering for each data set.
Transgenerational Education in Pandemic Time: Dromology Approach of Digital Learning Environment

This article seeks to relate (trans)generational issues with the phenomenon of sociodromology and education in the COVID-19 pandemic period that began globally in the first months of 2020 and 2021. To make such crossings possible, we will initially present under which aspects we are addressing the term generation, and its respective phenomenological development, transgenerationality. Once these concepts have been properly presented it is also necessary to relate those information with the generations that are currently living within educational environments, especially schools. After such an exposition, we will present the theory that contemplates the vector of acceleration and its respective consequence for humanity. Sociodromology emerges as a theoretical instrument that makes it possible to critically glimpse how the speed of machines has dictated our lives, having a perverse facet of exclusion that determines which groups may be able or unfit at their pace. Finally the text concludes its reflection on the current educational context using the critical tools of socio-pedagogy, which views the subjects who are involved in the educational process as representatives of an even greater order in the world, which transcends schools. In this perspective, education as a pillar of knowledge acquisition should be revised in addition to its pre-determined role in society and should form individuals who could change the world and their communities.
Development of local wisdom-based thematic teaching materials to improve adaptive cultural literacy

This community service was carried out because Indonesian junior high school teachers in Bengkulu City still lacked skills, expertise, and knowledge on how to incorporate culture and local wisdom while creating educational materials. The method chosen was training and mentoring in the development of teaching materials which was attended by 48 Indonesian language teachers from SMP/MTs. The teaching materials developed are in the form of Student Worksheets (LKPD). The outcomes of PkM demonstrate that teachers' understanding of the importance of teaching materials based on local wisdom to enhance adaptive cultural literacy has risen after taking part in a number of activities. Furthermore, there has been a noticeable rise in the ability of Indonesian junior high school teachers in Bengkulu City to create instructional materials for the Indonesian language based on local wisdom. Evidently, over the course of a month, a number of teacher teams were successful in creating LKPD with the primary content drawn from folklore, legends, traditional ceremonies, and cultural objects.
AJAR UP 2021 as a basic introduction to sign language learning

AJAR UP is one of the activities of the Environmental Engineering study program students at Universitas Pertamina, realizing good learning for the community. During this period, AJAR UP focused on sign language learning activities. AJAR UP activities aim to expand soft skills obtained in the academic field and in the non-academic field in the form of language, which can later be implemented in the workplace for the general public and also for Universitas Pertamina students. This activity was attended by 28 people on Saturday, December 18, 2021. AJAR UP activities so far, which have been open for free, have opened new learning opportunities for the general public and Universitas Pertamina students to learn sign language. Based on the feedback provided, it can be concluded that the participants stated that sign language is essential to learn, especially in today's era. Therefore, AJAR UP activities indirectly open up learning opportunities for the community and Universitas Pertamina students about culture and some of the methods used in sign language.
Exact Spectral Norm Regularization for Neural Networks

We pursue a line of research that seeks to regularize the spectral norm of the Jacobian of the input-output mapping for deep neural networks. While previous work rely on upper bounding techniques, we provide a scheme that targets the exact spectral norm. We showcase that our algorithm achieves an improved generalization performance compared to previous spectral regularization techniques while simultaneously maintaining a strong safeguard against natural and adversarial noise. Moreover, we further explore some previous reasoning concerning the strong adversarial protection that Jacobian regularization provides and show that it can be misleading.
THE ROLE OF PARENTS IN CAMPAIGNING CHILDREN TO CONTINUE LECTURING FOR STUDENTS IN THE REGION

There are still some areas where students decide not to continue to higher education after graduating from high school due to various factors. This study aims to attract a student to want to take higher education.The design in this study used a descriptive method through a qualitative approach. The samples taken in this study were as many as 26 respondents via a google form. The instruments used are observation sheets and interviews.From the results of data testing, it was found that the areas with a lot of interest in continuing to study were mostly only in big cities, which became an obstacle to their interest in studying for regional students, namely because of the lack of access to information, as well as parental concerns about limited funds.
Orthogonal decomposition of multivariate densities in Bayes spaces and its connection with copulas

Bayes spaces were initially designed to provide a geometric framework for the modeling and analysis of distributional data. It has recently come to light that this methodology can be exploited to provide an orthogonal decomposition of bivariate probability distributions into an independent and an interaction part. In this paper, new insights into these results are provided by reformulating them using Hilbert space theory and a multivariate extension is developed using a distributional analog of the Hoeffding-Sobol identity. A connection between the resulting decomposition of a multivariate density and its copula-based representation is also provided.
Flood mitigation education for the community of Dul Village, Central Bangka

Floods are a regular phenomenon in Indonesia, particularly in the Bangka Belitung region. Natural elements such as soil conditions and excessive rainfall, as well as non-natural variables such as rubbish, concretization in infiltration zones, development in floodplain areas, and so on, all contribute to flood disasters. Dul Village, Pangkalanbaru District, Central Bangka Regency, was one of the flood-affected areas. To assist the community in overcoming issues, community service activities will be carried out with the long-term goal of establishing a flood response community, with the specific goal of providing flood disaster mitigation education to the community. A flood disaster socialization program and an early warning system for the community are among the methods employed to carry out this program. The outcomes of this program have increased public awareness of the potential and causes of flooding, particularly in Dul Village, as well as the function of the early warning system in mitigating flood-related losses.
World-Class Scientific and Educational Centers as Drivers of the Formation of the Human Capital of Russians: Sociological Analysis of the Problem Field (to the 300th Anniversary of Saint Petersburg State University)

Introduction. World-class scientific and educational centers (REC MU) are considered as special social organizations that form one of the promising social institutions in Russia. REC MU are becoming one of the real opportunities to achieve a synergistic effect in the formation of human capital. They are regarded as analogues of think tanks that are dynamically operating in the West and are gaining strength in the countries of the East. The purpose of the article lies in a sociological analysis of the activities of the REC MU and the identification of the prospects for their development as drivers for the formation of the human capital of Russians on the basis of turning to values and motivations, supporting creative and talented Russian youth. Methodology and sources. The methodological basis of the study was formed by the principles of analysis of social institutionalization, which allow studying the activities of the REC MU as an emerging new social institution with its own rules, norms, reflection and self-regulation. Another part of the methodological principles concerns the consideration of human capital as associated with the value-motivational attitudes of the individual. The empirical base was made up of data from the open press on the structure of activity and the problems of activity, the results of the author's empirical research and expert interviews with the leaders of the REC MU. Results and discussion. The first result is to fix and analyze the directions and orientations of the activities of the REC MU, which at the normative level form a common system of routes and strategies for their development. The data of this analysis were obtained as a result of the study of publications. The second result of the study fixes the vectors of the reversal of the activities of the REC MU in the direction of the formation of the human capital of Russians in the regions where the REC MU have been created. The data of this result were obtained as a result of summarizing the materials of the author's empirical study of one of the operating universities included in the REC MU. Another result of the study, obtained as a result of expert interviews with leaders and managers of 12 REC MU, shows the problematic field of the institutionalization of these centers. Conclusion. REC MU as a specific social institution are going through the stage of their formation. As for the activation of the human capital of the regions, this will require additional efforts and potentials, the development and implementation of modern methodologies and technologies for educational activities and information work, in particular, with student youth. The existing gap between value-motivational structures and their exclusion from the system of analysis of human capital is scientifically untenable, but in a practical sense it negatively affects real activity.
aSTDP: A More Biologically Plausible Learning

Spike-timing dependent plasticity in biological neural networks has been proven to be important during biological learning process. On the other hand, artificial neural networks use a different way to learn, such as Back-Propagation or Contrastive Hebbian Learning. In this work we introduce approximate STDP, a new neural networks learning framework more similar to the biological learning process. It uses only STDP rules for supervised and unsupervised learning, every neuron distributed learn patterns and don' t need a global loss or other supervised information. We also use a numerical way to approximate the derivatives of each neuron in order to better use SDTP learning and use the derivatives to set a target for neurons to accelerate training and testing process. The framework can make predictions or generate patterns in one model without additional configuration. Finally, we verified our framework on MNIST dataset for classification and generation tasks.
Reasoning about Moving Target Defense in Attack Modeling Formalisms

Since 2009, Moving Target Defense (MTD) has become a new paradigm of defensive mechanism that frequently changes the state of the target system to confuse the attacker. This frequent change is costly and leads to a trade-off between misleading the attacker and disrupting the quality of service. Optimizing the MTD activation frequency is necessary to develop this defense mechanism when facing realistic, multi-step attack scenarios. Attack modeling formalisms based on DAG are prominently used to specify these scenarios. Our contribution is a new DAG-based formalism for MTDs and its translation into a Price Timed Markov Decision Process to find the best activation frequencies against the attacker's time/cost-optimal strategies. For the first time, MTD activation frequencies are analyzed in a state-of-the-art DAG-based representation. Moreover, this is the first paper that considers the specificity of MTDs in the automatic analysis of attack modeling formalisms. Finally, we present some experimental results using Uppaal Stratego to demonstrate its applicability and relevance.