Reviewerly/exHarmony huggingface dataset:
Asynchronous and composable distributed co-simulation engine design for cloud-based industrial CAE software

The industrial Internet can be seen as a combination of industrial software, sensors, networks and cloud platforms. Among them, industrial software is the key to data utilization while the cloud platform provides support for Industrial Internet. Through "visualization", "structuring", "modeling and simulation" and other means, industrial software can help solve the design and manufacturing problems of industrial products. But the difference between industrial Internet and traditional Internet lies in the different application scenarios and data characteristics, which also leads to the different emphasis of system architecture design. Considering the needs of high concurrency and huge data processing, the distributed system must provide high performance of communication and computing services. In addition, with the continuous expansion of the simulation model and the simulation scale, it becomes particularly significant to reduce the model development complexity and improve maintainability and flexibility. To make the distributed co-simulation engine more high-performance, we propose an asynchronous communication module as the underlying architecture of the distributed engine. Then combining the discrete event system specification and Actor model, a layered architecture has been proposed, which implements the modular programming of behavioral model by functional programming. Based on the architecture mentioned above, a distributed CAE cloud platform is proposed, which is hierarchical, modular, composable and reusable.
CIVSCOPE: Analyzing Potential Memory Corruption Bugs in Compartment Interfaces

Compartmentalization decomposes a program into separate parts with mediated interactions through compartment interfaces---hiding information that would otherwise be accessible from a compromised component. Unfortunately, most code was not developed assuming its interfaces as trust boundaries. Left unchecked, these interfaces expose confused deputy attacks where data flowing from malicious inputs can coerce a compartment into accessing previously hidden information on-behalf-of the untrusted caller.
Generalized Net Model of the General Claim Process – Cassation Proceedings before the Supreme Court of Cassation

In the present paper an application of the apparatus of Generalized nets is proposed for modeling the cassation proceedings before the Supreme Court of Cassation. The advantages of using such model are very different, some of them are expressed in helping to detect and eliminate unnecessary complications in the process itself, leading to its simplification and optimization, presenting a complex process simpler and more understandable, for scientific and teaching needs etc.
Pelatihan Pengelolaan Website Desa Sebagai Media Informasi Digital Di Desa Fafinesu C, Kecamatan Insana Fafinesu, Kabupaten Timor Tengah Utara

Digital information media allows the disclosure of public information and supports development. Website as a digital information media is an important way to be developed especially in the field of Government. Content Management System (CMS) is an alternative website creation without using programming. To facilitate village operators in creating and managing village information digitally, training of village website management as an information media is an important thing to do. Fafinesu C village at Insana Fafinesu District, North Central Timor Regency is the target in this program. The problems in Fafinesu C village are information system of village has not been managed properly, the absence of villace information system and the village officials still not understand about management information. The program is collaboration with Insana Fafinesu Subdistrict and Fafinesu C village. With this program, village operators can create a village website using a CMS. Website of Fafinesu C villages can publish the information about the village. The operator of village can manage the website independently. In addition, through the village website, village potentials can be published so that the village are able to develop the potential that exist.
Analysis of the Needs of Child and Youth Welfare Workers: Focusing on Borich Needs and LF Model

Objectives The purpose of this study was to provide basic data to explore the direction of practice of strengthen-ing the professional competency of child and youth welfare workers.&#x0D; Methods The subjects of the study were collected from 163 child and youth welfare workers from July 2022 to March 2023. The measurement tools were based on 14 standards of The NASW Standards for Social Work Practice in Child Welfare of the National Association of Social Workers.&#x0D; Results As a result of the study, according to the method of deriving the priorities of Borich need and The Locus for Focus model, the common high needs were ‘Administration’, ‘professional development’, ‘qualification’, and ‘collaboration’. And the next needs were ‘advocacy’ and ‘supervision’.&#x0D; Conclusions Based on the research results, strengthening qualification standards for professional development and reinforcement, preparing a systematic supervision system, institutional reorganization to improve excessive workload and caseload, opening of essential courses in conservative education to create an appropriate horizontal communication system and flexible organizational culture, active advocacy activities was suggested.
Mineralogy and Geochemical Characteristics of Matamba Kaolin Deposit-Njombe Region South-Western Tanzania: Implications for Industrial Applications

Kaolin is a commercial clay material composed of hydrated aluminosilicate mineral kaolinite and used in various industrial applications such as ceramics, paper, paints, refractories, fiberglass, plastics, cosmetics and pharmaceuticals. It is formed as a result of strong chemical weathering of crystalline and feldspar-rich rocks or hydrothermal alteration of granitic rocks at relatively low temperature and pressure conditions. The kaolin deposit of Matamba originated from the weathering of leucogabbro rock during the development of the African land surface. In view of highlighted properties, the mineralogy and chemical characteristics of the Matamba kaolin deposit were investigated to determine its industrial applications. As part of the study approach, forty-six (46) samples were collected and analyzed for major oxides using X-ray fluorescence (XRF) and mineralogical composition using X-ray diffraction (XRD). The XRD results indicated that the Matamba kaolin is dominantly composed of kaolinite (10.1–100%) with other phases such as albite (1.2–56.8%), oligoclase (10.1–54.3%), quartz (1.2–33.9%), goethite (1.0–9.4%) and muscovite (1.1–29.5%). The dominant major oxides are SiO2 (39.78–67.96 wt.%), Al2O3 (14.60–38.07 wt.%) and subordinate amounts of Fe2O3 (0.93–6.37 wt.%), MgO (1.42–4.74 wt.%), Na2O (0.10–1.09 wt.%), K2O (0.14–2.01 wt.%), CaO (0.08–0.99 wt.%), TiO2 (0.07–1.66 wt.%), P2O5 (0.36–1.77 wt.%) and LOI (1.91–13.97 wt.%). These major oxides correlate with the mineralogical composition supporting kaolinite dominance. Consequently, compared with some industrial specifications, these results indicate that Matamba kaolin deposit may be useful for ceramic products, refractories such as fireclay crucibles and electrolytic production of aluminium and its alloys. However, it should be beneficiated and upgraded to improve some technical properties to qualify for other industrial applications.
Analisis Metode Backpropagation Dalam Memprediksi Jumlah Produksi Daging Kambing Di Indonesia

A science that has always developed rapidly until now is artificial neural networks. A computational science that works like the human nervous system is an artificial neural network. Artificial neural networks with the backpropagation method can make a prediction on data. In this article, a prediction will be made on the amount of goat production in Indonesia. Goats are one of the livestock that can produce nutritious meat. The lack of goat meat will cause the price of goat meat to rise. Producing enough goat meat helps stabilize the price of meat, but if goat meat production is less than demand, it will lead to price increases. Therefore, looking at the problems above, this study aims to predict goat meat so that in the future it can know how much goat meat must be predicted by processing data first and then being used as input in predicting the amount of goat meat production. Prediction is one way to estimate future demand. Avoiding the lack of meat availability, by predicting the amount of goat meat produced in such a way that there is no scarcity of goat meat and fluctuations in the price of goat meat in the market. Basic methods and data are required to make predictions. In this study, data was obtained from BPS Indonesia in the livestock section using data from 2001-2021 as training data and 2002-2022 as test data. The method applied in this article is the backpropagation algorithm. This article applies 5 network architectures implemented in the mathlab application. The architecture used in this article is 20-25-1 with a Mean Squred Error testing 0.00447765, in 20-30-1 architecture produces Mean Squred Error 0.00300466, in 20-35-1 architecture produces 0.00426823, in 20-37-1 architecture produces 0.00357757. Based on the best architecture produced in this study, the 20-15-1 architecture with 90% accuracy with a Mean Squared Error testing 0.00262384 at epoch 27915 Iterations. Thus it can be concluded that the backpropagation algorithm can provide good accuracy in the prediction process. With this research, the livestock industry can utilize it as one of the materials to predict goat meat in the future
Abnormal Emotion Recognition Based on Audio-Visual Modality Fusion

In indoor places, such as homes or offices, when abnormal events occur, the behavior and voice of individuals or groups will display abnormal signals. These signals can be both visual and auditory, and they interact and complement each other to jointly create a sense of emotional atmosphere within the scene. In order to achieve effective and accurate perception and response of abnormal emotion during the interaction in smart home, a model of abnormal emotion recognition based on audio-visual modality fusion is proposed. Human skeleton motion data and audio data are utilized to construct separate deep learning networks for action recognition and speech emotion recognition. The accuracy rate achieved on the G3D dataset is 100% and the accuracy rate achieved on the CASIA corpus is 90.83%. For decision-level multimodal fusion, the predicted results of actions and speech emotions are mapped to the “abnormal” axis through fuzzification and weighted average methods. In this process, considerations are taken into account for the varying contributions of different speech emotions and behaviors to the abnormal emotion, as well as the recognition recall rates of the unimodal emotion models. Then the two modalities are allowed to mutually modify each other and achieve quantitative analysis of abnormal emotion through weighted additive fusion.
Evaluating Link Prediction Explanations for Graph Neural Networks

Graph Machine Learning (GML) has numerous applications, such as node/graph classification and link prediction, in real-world domains. Providing human-understandable explanations for GML models is a challenging yet fundamental task to foster their adoption, but validating explanations for link prediction models has received little attention. In this paper, we provide quantitative metrics to assess the quality of link prediction explanations, with or without ground-truth. State-of-the-art explainability methods for Graph Neural Networks are evaluated using these metrics. We discuss how underlying assumptions and technical details specific to the link prediction task, such as the choice of distance between node embeddings, can influence the quality of the explanations.
Analisis Model Backpropagation Dalam Meramalkan Tingkat Penjualan Saldo “Link Aja”

Analysis of a prediction (forecasting) is very important in a study, so that research becomes more precise and directed (Wanto and Windarto, 2017). As is the case in predicting the level of Link Aja's balance sales. This research is expected to be useful for an agency as one of the study materials in business development. A system to predict the level of sales of Link Aja balance at PT. Wahana Putra Yudha. Artificial Neural Network is a method that is able to perform a mathematical process to predict the level of sales of Link Aja Balance at PT. Wahana Putra Yudha. By using the backpropagation method, the previous data processing process is carried out which will be used as input to predict the sales level of Link Aja Balance. The data were taken from January 2021 to April 2022. January 2021 to August 2021 were used as training data, while September 2021 to April 2022 were used as test data. The training architecture model used to predict the sales level of Link Aja's Balance is: 4-2-1; 4-25-1; 4-50.1; 4-75-1; and 4-100-1. The best architecture is 4-50-1, the percentage result is 75% in each test
Hypergraph (Beta): v0.10.0 release

Every 28th of the month, we release a new version of Hypergraph — we just released Hypergraph (Beta) v0.10.0 [https://libscie.org/hypergraph] 🎉 (if you already have it installed, it'll auto-update next time you run it). Below, you can find a detailed and human-readable version of everything that's changed since last months release (v0.9.0) [https://libscie.org/hypergraph-beta-v0-9-0-released/]. Added * Markdown rendering in Hypergraph (include
Strangelove Vector Graphics (Or: How I Learned to Stop Worrying and Love SVGs)

Over the past month, I participated in a workshop led by Cassie Evans [https://twitter.com/cassiecodes] and Smashing Magazine [https://www.smashingmagazine.com] called "The SVG Animation Masterclass [https://smashingconf.com/online-workshops/workshops/cassie-evans/]". It was a fantastic workshop and inspires me to do more with animations for liberate science designs in the future.
[Utilization of remote psychosocial intervention with men: What is the state of knowledge?]

Background Remote psychosocial intervention has been used by most health care organizations since the beginning of COVID-19 pandemic. However, the rapid introduction of this type of practice generates new methods of intervention that raise many questions, particularly about men who, in general, use face-to-face psychosocial intervention less than women. This documentary research aims to report on current knowledge on remote psychosocial intervention with men. Methods PICO technique was used to find relevant documents to achieve the objective of this research. In accord with our criteria, 62 documents were selected in several databases and search engines. The selected texts were subject to an analysis process consisting of two stages: the creation of reading sheets followed by a content analysis. Results The results underline the lack of scientific data on the men's experience when they initiate a request for help, the commitment process, and the effectiveness of the remote intervention. Several elements seem potentially promising, including patients' perceptions of having more power and freedom which could favorize engagement of men who have a traditional vision of masculinity. Conclusions It appears that gender-related variables are not commonly used when it comes to analyze the effects of distance psychosocial intervention. Other studies will be needed to have a holistic vision of the realities experienced by men towards the offer of remote psychosocial intervention.
Dynamic Semi-Supervised Federated Learning Fault Diagnosis Method Based on an Attention Mechanism

In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer. A federation strategy driven by an attention mechanism was designed to filter out the unreliable information hidden in the local model. SSFL-ATT can ensure the federation model’s performance as well as render the unlabeled client capable of fault classification. In cases where there is an unlabeled client, compared to the existing semi-supervised federated learning methods, SSFL-ATT can achieve increments of 9.06% and 12.53% in fault diagnosis accuracy when datasets provided by Case Western Reserve University and Shanghai Maritime University, respectively, are used for verification.
Based on the Typical Differences between Chinese and English, the Practical Problems and Solutions in English

Although there are differences between Chinese and English as two different languages, one of their common points is that they both carry their own cultures, and culture is one of the "key nodes" that can connect the two. Therefore, in order to achieve "common language structure" on the basis of "learning home and abroad", it is necessary to use English as a tool to spread Chinese culture, and then promote the dissemination, communication and integration of different cultures, which is also one of the important purposes of learning English. At the same time, enhancing cultural confidence is one of the important cultural strategies of the Chinese government in recent years. In order to better let the world listen to the voice of China and enhance students' cultural identity, "telling Chinese stories well in English" has become one of the distinct themes in the field of English teaching. However, due to the inherent differences in Chinese and English in vocabulary, sentences and discourse, as well as the particularity of traditional Chinese culture, students will have great problems in vocabulary memory, sentence translation and discourse reading in the teaching process of telling Chinese stories in English. therefore, In the process of middle school English teachers should be based on the typical differences between Chinese and English and the characteristics of traditional Chinese culture, Starting with the vocabulary, sentences and texts of traditional Chinese culture, By classifying traditional Chinese vocabulary, distinguishing the style characteristics of sentences and emphasizing the structural characteristics of traditional Chinese culture, To solve the problems of middle school students in vocabulary, sentences and discourse, While paying attention to students' cultural knowledge and pragmatic ability, Both input and output, To solve the problem of students learning traditional Chinese culture in English step by step, Improve the comprehensive quality, Be an envoy of cultural communication and communication, Truly achieve in the "learning of Chinese and Western" on the basis of "language structure common".
Optimizing Upstream Representations for Out-of-Domain Detection with Supervised Contrastive Learning

Out-of-Domain (OOD) text detection has attracted significant research interest. However, conventional approaches primarily employ Cross-Entropy loss during upstream encoder training and seldom focus on optimizing discriminative In-Domain (IND) and OOD representations. To fill this gap, we introduce a novel method that applies supervised contrastive learning (SCL) to IND data for upstream representation optimization. This effectively brings the embeddings of semantically similar texts together while pushing dissimilar ones further apart, leading to more compact and distinct IND representations. This optimization subsequently improves the differentiation between IND and OOD representations, thereby enhancing the detection effect in downstream tasks. To further strengthen the ability of SCL to consolidate IND embedding clusters, and to improve the generalizability of the encoder, we propose a method that generates two different variations of the same text as "views". This is achieved by applying a twice "dropped-out" on the embeddings before performing SCL. Extensive experiments indicate that our method not only outperforms state-of-the-art approaches, but also reduces the requirement for training a large 354M-parameter model down to a more efficient 110M-parameter model, highlighting its superiority in both effectiveness and computational economy.
Dynamic Group Parameter Modeling for Click-Through-Rate Prediction

It is noted that Click-Through-Rate(CTR) prediction plays an important part in recommendation systems and online advertising. Over the past few years, numerous studies have been conducted to improve the accuracy of CTR prediction by exploring data inherent patterns. These studies indicate that training CTR models with group-specific parameters on divided data groups can lead to significant improvements. However, most works generally divide groups manually with some prior knowledge, and such a fixed group division method may hinder the expression of user common interests. To address this limitation, we propose a novel group parameter modeling method, where the user group division and group parameter learning processes are completed in an automatic and dynamic way. Our method employs a three-stage approach, consisting of group information selection, group representation learning, and group parameter generation, which allows the efficient expression of user common interests. We conduct experiments on both public datasets and industrial datasets, and the experimental results demonstrate the effectiveness of our method. We have also deployed the model in an online advertising system and observed significant improvements in both CTR and Revenue Per Mille (RPM).
EFFECTIVENESS OF CAKE APPLICATION ON STUDENT'S VOCABULARY MASTERY

This study aims to determine the effectiveness of using the Cake application for students' English vocabulary learning. This research is a quantitative research which is a pre-experimental study with a one-group pre-test post-test design. The population of this studywere students of the Bhinneka PGRI University English Education study program in the 2022/2023 academic year. The sample of this study were 8th semester students, totaling 19 students. The sample used by the researcher is a purposive sampling type. The instruments used were pretest and posttest. In the pretest, students are asked to do a test about English vocabulary before using the cake application. In the posttest, students were also asked to work on test questions after using the cake application. The results showed that the use of the cake application affected students' English vocabulary mastery. This can be seen from the results of data analysis that has been carried out from the pretest average value of 72.37, as a comparison to the posttest value of 83.42. In addition, it is also proven again by using hypothesis testing paired sample t-test. The result obtained is 0.003. In the paired t-sample test rules, if the significance is less than 0.05, then H0 is rejected and Ha is accepted. Based on the results of the study, it can be concluded that the use of the cake application affects students' English vocabulary learning