Reviewerly/exHarmony huggingface dataset:
Information security outsourcing in a resource-sharing environment: The impacts of attack modes

Information security outsourcing has become an emerging trend in the operations of information security, but the relation between information assets of firms and attack modes of hackers have failed to be considered. Through building a game-theoretic model, this article analyzes security outsourcing of two firms who share their information resource with each other and are confronted with opportunistic attacks and targeted attacks. We find that in the case of security decisions in-house, the firms may obtain a lower expected cost and the hacker may derive a lower expected benefit under targeted attacks than under opportunistic attacks, even though targeted attacks are widely deemed to be more harmful to the firms. When outsourcing security operations to a MSSP (Managed Security Service Provider), we reveal that under targeted attacks the MSSP can reap a higher expected benefit and the hacker can still derive a lower expected benefit. Finally, we examine the effects of key security elements and find some interesting results. In particular, the MSSP may or may not benefit from the degree of resource sharing, and the hacker may suffer from its learning ability.
A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis

A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users' latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world application scenarios. To address this issue, this paper proposes a novel hybrid recommendation model that combines Autoencoder and LFA techniques, termed AutoLFA. The main idea of AutoLFA is two-fold: (1) It leverages an Autoencoder and an LFA model separately to construct two distinct recommendation models, each residing in a unique metric representation space with its own set of strengths; and (2) it integrates the Autoencoder and LFA model using a customized self-adaptive weighting strategy, thereby capitalizing on the merits of both approaches. To evaluate the proposed AutoLFA model, extensive experiments on five real recommendation datasets are conducted. The results demonstrate that AutoLFA achieves significantly better recommendation performance than the seven related state-of-the-art models.
Utilization Of Benime Application for Speaking Learning

Here the researchers conducted a study using the Benime application, the Benime application is one of the media that can help educators to create interesting teaching materials and has many features that can be used. Benime application-based learning videos. In this study, the researcher used a qualitative research method, which requires data that is in accordance with the original, in which the researcher used an interview with one of the teachers at Madrasah Aliyah Negri 2 Koto Baru Padang Panjang, making observations by descending To that location. The benefit of this application is that it can help educators make a learning video, and also provide benefits to students in understanding a given material, and can have a positive impact on students by providing critical thinking about the material provided by educators. So that with the Benime application, it can facilitate the activities or activities of educators and students in learning speaking.
Uma Revisão Qualitativa do uso da Blockchain na Infraestrutura de Veículos Elétricos

Due to the popularity of Blockchain technology, there have been many proposed applications of it in the Vehicle Electric Infrastructure (VEI). In this way, this work presents an overview of some proposals based on their Blockchain’s utilization. Therefore, it provides a qualitative analysis of them and gives an overview of Blockchain technology and consensus algorithms. Finally, we indicate some future works and opportunities.
A trajectory outlier detection method based on variational auto-encoder

&lt;abstract&gt; &lt;p&gt;Trajectory outlier detection can identify abnormal phenomena from a large number of trajectory data, which is helpful to discover or predict potential traffic risks. In this work, we proposed a trajectory outlier detection model based on variational auto-encoder. First, the model encodes the trajectory data as parameters of distribution functions based on the statistical characteristics of urban traffic. Then, an auto-encoder network is built and trained. The training goal of the auto-encoder network is to maximize the generation probability of original trajectories when decoding. Once the model training is completed, we can detect the trajectory outlier by the difference between a trajectory and the trajectory generated by the model. The advantage of the proposed model is that it only needs to compute the difference between the original trajectory and the trajectory generated by the model when detecting the trajectory outlier, which greatly reduces the amount of calculation and makes the model very suitable for real-time detection scenarios. In addition, the distance threshold between the abnormal trajectory and the normal trajectory can be set by referring to the proportion of the abnormal trajectory in the training data set, which eliminates the difficulty of setting the threshold manually and makes the model more convenient to be applied in different actual scenes. In terms of effect, the proposed model has achieved more than 95% in accuracy, which is better than the two typical density-based and classification-based detection methods, and also better than the methods based on machine learning in recent years. In terms of efficiency, the model has good convergence in the training phase and the training time increases slowly with the data scale, which is better than or as the same as the comparison methods.&lt;/p&gt; &lt;/abstract&gt;
Efektivitas Technode Hydrogel 0,02% Sebagai Penyembuhan Luka Post Operasi Ovariohisterectomi dan Orchhiectomi Pada Kucing Domestik (Felis silvestris Catus) Dibandingkan Iodine 10%

Antibiotics have been reduced to support Anti-Microbial Resistance (AMR) campaigns worldwide due to widespread bacterial resistance to commonly used antibiotics, the emergence of new superbugs or diseases, and the impact of antibiotic waste on the environment and soil. Iodine is generally used as an alternative because it has antibacterial and antifungal potential, suitable for preventing wound infections. However, iodine can cause a serous exudative effect and quickly become inactive when mixed with serous fluids. The study aims to find alternative therapeutic preparations to replace antibiotics when treating open wounds. Experimental research in which domestic cats were divided into two groups. Group A was given 10% iodine therapy twice a day, while Group B was given Technode Hydrogel 0,02% therapy twice a day for seven days. The wound score was calculated based on three points with the following criteria: discharge, swelling, erythema, and dehiscence. The scoring system was adopted as suggested by Sylvestre. The results showed that Technode Hydrogel 0,02% had an average wound score of 0,457 compared to 10% iodine which was 0,714. Hydrogel Technode is expected to be the best alternative for wound care, both in terms of efficacy and efficiency, to help the healing process of open wounds.
The Effectiveness of Acid and Base Android-Based Ludo Chemistry Game Media on Students' Cognitive Learning Outcomes

This research aims to analyze the level of effectiveness of the chemical ludo game on students’ cognitive learning outcomes in class XI SMA/MA. This method of research used a Quasi-Experimental with a non-equivalent control group design. The population of this research was students of class XI MIPA SMAN 3 Tualang. The sampling technique in this research was purposive, so class XI MIPA 1 was selected as the control class and XI MIPA 2 as the experiment class. The research instrument used in this study was the learning achievement test. The data analysis technique used was the N-Gain test, and hypothesis testing using the t-test. The results showed that students’ learning outcomes in the experimental class increased compared to the control class. It was evidenced by the average post-test score of 64.545 for the experimental class and 79.091 for the control class. The results of the N-Gain test for the experimental class were in the high category, namely 0.73 and for the control class, 0.57 in the medium category. The hypothesis test showed that at the real level α = 0.05 with dk = 78, the value of count (5.1413) &gt; table (1.8044). Acid and base android-based ludo chemistry game media effectively improved cognitive learning outcomes of class XI SMA/MA students.
Strategy for Developing Creative Thinking Skills in Learning to Write Poetry for Junior High School Students

This study aims to describe the strategy for developing creative thinking skills and the functions and benefits of developing creative thinking skills in learning to write poetry for junior high school students. The method used was a descriptive method with a qualitative approach. Data collection was carried out using interviews and questionnaires. The informants in the study were 12 Indonesian Language and Literature learning teachers in the East Java region, including Kediri, Gresik, Malang, Madura, Jombang, Banyuwangi, Lamongan, Kalimantan. The data analysis technique used was discourse analysis, which analyzed oral and written answer sentences. Based on the data, it can be concluded that the learning strategies for writing poetry carried out by teachers were diverse, including word picking, word-priming strategies, word chat cards, cooperative learning (roulette writing), modeling, photos/events that have been experienced, objects, directly seeing things, pictures, word clusters, a combination of models, one event one poem, one picture one poem, ATM, Student Teams Achievement Division (STAD), keyword strategies, one event one poem, word collections, and experiences. From the method used by the teacher, it turned out that it could improve creative thinking skills based on the stages of creative thinking proposed by Torrance, including fluency, flexibility, originality, and detail in choosing themes, diction/word choice, rhymes, typography, imagery, mandate, and the cohesion of stanzas.
The Effect of Ornamental Plants on Reducing the Intensity of Electromagnetic Wave Radiation

Entering the digital era, the culture of human life is increasingly inseparable from the use of electromagnetic waves which are very useful in supporting human life, but in the other side, it also has negative impacts in the form of radiation that threatens human health. Only few people realize that ornamental plants have many benefits other than as a decoration inside or outside the room. Several types of ornamental plants have the ability to absorb electromagnetic radiation emitted by electronic equipment. In this simple physics research, measurements were made to compare the absorption of several types of ornamental plants to electromagnetic wave radiation, those are karet kebo, betel, succulent, ivy plant, and snake plant. The research results show that snake plant’s ability to absorb electromagnetic wave radiation is the greatest than others. Research conducted on ivy shows that the absorption of ornamental plants to electromagnetic wave radiation is influenced by the distance of the plant to the radiation source, where the closer the distance between the ornamental plants to the source of electromagnetic wave radiation is, the greater the absorption of radiation occurs, resulting in a decrease in the intensity of electromagnetic radiation also getting bigger. The trend of changes in the absorption of electromagnetic wave radiation shows a decrease in each increase in distance according to the exponential graph.
‘Home Visit' as a Learning Method for Early Children During the Covid-19 Pandemic

This descriptive, quantitative, and qualitative research aims to describe early childhood learning methods during the COVID-19 pandemic in Palopo City. This study is motivated by several problems; the lack of parental involvement in accompanying children to study at home, economic factors, the internet network, and a lack of interest in children participating in learning so that early childhoodcannot develop optimally. Data was gathered through interviews and questionnaires administered via the media google form to 69 teachers and principals in Palopo City.Data analysis uses source triangulation with the stages of data collection, data reduction, data display, and conclusion. This study's results indicate that teachers still use children's worksheets (LKA) assignments for learning during the COVID-19 pandemic. This study's novelty is that teachers use a new approach, namely, the visit-home method. Even though there are still a few, who use the home visit method, the home visit method is considered beneficial because the learning carried out is more effective compared to online. Students understand learning material more quickly and are interested in participating in learning at home. Researchers hope this home visit method will not only be applied during a pandemic.
Decentralized E-Voting System Using Blockchain

Voting is a primary right of every citizen living ina country. Traditional methods used for voting includes paper ballot system, EVMs (Electronic Voting Machines), etc. which are still followed and trusted by every voter or citizen blindly. These voting systems can have ambiguity as the data is maintainedunder a centralized environment whether it is counting the paper ballots or storing the vote caste on a computer server. Thisuse of a centralized database for the voting system has some security issues such as Data modification through the third party in the network due to the use of the central database systemas well as the result of the voting is not shown in real-time, or manipulation with the data which can hamper the result and thus have an impact on not only system integrity but also lose faith in democracy, government, nation, etc. The votingmethods used in an election should be legal, accurate, safe,and convenient. Keywords: Blockchain Technology, EVM(ElectronicVotingSystem), Smart Contracts, Ethereum, Solidity, etc.s
Active Learning Impact in the Annotation of Cases in a Pathology-PACS

Clinical pathology has been adopting digital technologies in the review process of the slides. This paradigm shift is being instigated by the advances in technology that allow pathologists to remotely access data banks and visualize high-resolution images with the support of cutting-edge imaging tools. Computer-assisted diagnosis is one of such improvements that is proliferating due to the recent advancements in deep learning technologies. In this clinical area, these tools are especially relevant, due to the big spatial resolution of these images. They can identify regions of interest or diagnostically relevant features on the image, improving the screening times. This article presents the use of active learning strategies to train an object detection model for the mitotic annotation use case, developed in the iPATH research project. The model was successfully integrated into a Pathology-PACS and is being actively used by pathologists for research purposes. Results regarding the evolution and development of this model are also presented in this work.
Exploratory Data Analysis in Electronic Health Records Graphs: Intuitive Features and Visualization Tools

Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory Data Analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, attributes, and high cardinality, exploring the different datasets and finding patterns and insights become laborious and prone to errors. In this work, we propose GraF- Eda for EDA over EHR data from different institutions. GraF-EDA models EHRs as time-evolving graphs, allowing the interoperability of such data into a single representation. We extract meaningful features from the graph nodes and provide intuitive visualizations to improve data explainability. We evaluate GraF-EDA with four COVID-19 datasets from hospitals of the São Paulo state, Brazil, resulting in million-scale graphs. Our method identified correlations, similarities and dissimilarities among medical treatments, exams, clinics, and outcomes. With the visual tools provided by GraF-EDA, we were able to spot cases of interest and check more details about them. Our results indicate that GraF-EDA is a fast, effective, open-sourced tool for EDA of EHRs from multiple institutions.
Image Semantic Segmentation Algorithm of DeepLabv3+ Based on Attention Mechanism and Strip Pooling

In order to solve the problem of unsatisfactory DeepLabv3+ segmentation of some small target objects and the details loss caused by only one scale coding features in the decoding stage, proposed a semantic segmentation algorithm based on attention mechanism and strip pooling . In the coding stage, a strip of attention refinement module is introduced after the backbone network to increase the receptive field in the horizontal and vertical directions of the coding stage and improve the perception ability of small target information. Refactoring ASPP modules with improved mixed strip pooling modules to enhance contextual information relevance and generate richer high-level semantic features. In the decoding stage, a spatial attention feature fusion module is designed to integrate multi-scale features extracted from the backbone network and enrich low-level semantic features. Experiments in Cityscapes datasets show that the algorithm can effectively enhance the feature extraction capability and small target perception, and alleviate the problem of detail loss.
CardioBERTpt: Transformer-based Models for Cardiology Language Representation in Portuguese

Contextual word embeddings and the Transformers architecture have reached state-of-the-art results in many natural language processing (NLP) tasks and improved the adaptation of models for multiple domains. Despite the improvement in the reuse and construction of models, few resources are still developed for the Portuguese language, especially in the health domain. Furthermore, the clinical models available for the language are not representative enough for all medical specialties. This work explores deep contextual embedding models for the Portuguese language to support clinical NLP tasks. We transferred learned information from electronic health records of a Brazilian tertiary hospital specialized in cardiology diseases and pre-trained multiple clinical BERT-based models. We evaluated the performance of these models in named entity recognition experiments, fine-tuning them in two annotated corpora containing clinical narratives. Our pre-trained models outperformed previous multilingual and Portuguese BERT-based models for cardiology and multi-specialty environments, reaching the state-of-the-art for analyzed corpora, with 5.5% F1 score improvement in TempClinBr (all entities) and 1.7% in SemClinBr (Disorder entity) corpora. Hence, we demonstrate that data representativeness and a high volume of training data can improve the results for clinical tasks, aligned with results for other languages.
TEACHING WRITING STRATEGIES USED BY ENGLISH TEACHERS AT MAN 2 KAMPAR

The objective of this research is to find out the strategies that teacher used in teaching writing at MAN 2 Kampar and its implementation. The method of this research was qualitative research. The subject of this research was English teacher in MAN 2 Kampar. Interview was used as its research instrument. The result of the research found that the teacher implemented chart strategy in teaching writing. Through the implementation of the strategy, the teacher is able to know how far the students could understand the material that the teacher has given and able to do the assignment as well.
Training on The Use of Edmodo as A Learning Media

The purpose of this training is to train teachers to use the online learning media platform Edmodo to make it easier for teachers to learn. The method used in this training was training and workshops. The participants in this training were teachers of SMP Negeri 3 Huamual, West Seram Regency, totaling 30 people, with a male percentage of 36% and a female of 64%. The steps in conducting the training are as follows: Conducting a survey at Huamual 3 Public Middle School, West Seram Regency, and identifying the problems faced by schools related to distance learning. (1) We analyze the needs of schools for widely used E-learning platforms. (2) Conduct a literature study on the use of the Edmodo application. (3) Prepare training support tools and materials. (4) Determine and prepare the material to be delivered. Training on the use of Edmodo Media in the learning process went well and smoothly. The results of this activity are: After this training, teachers will acquire new knowledge and skills in using the Edmodo-based learning environment in the teaching and learning process. The results of the evaluation through observation during training using Edmodo, the teacher was very enthusiastic, this was seen during the question and answer during the work practice.
ADAPTASI FLEKSIBILITAS SISTEM INFORMASI MANAJEMEN PENGELOLAAN RUMAH SUSUN SEDERHANA SEWA (RUSUNAWA) UNIVERSITAS TANJUNGPURA

The provision of information at Rusunawa Untan has increased in response to the needs of students for registration and data management. However, the system currently in use is still conventional and ineffective. This research aims to develop a web-based information system to manage registration and data at Rusunawa Untan. This system can send WhatsApp messages to residents and adapt to user needs. The flexibility of the information system is crucial in its development as the business environment is constantly changing. This research explains the development of the flexibility of the information system and the objectives to be achieved. The Object Oriented Analysis Design (OOAD) method is used for needs analysis, UML design, and implementation with PHP and the Laravel framework. The application is tested using black box method and User Acceptance Test (UAT) with 23 respondents. The UAT results show a satisfaction rate of 85.30%, indicating that the application meets expectations. The OOAD method is well implemented, and the application is well-received and able to adapt to changes in business requirements without significant modifications to the system.