Reviewerly/exHarmony huggingface dataset:
[Research on entity relationship extraction of Chinese medical literature and application in diabetes medical literature].

The medical literature contains a wealth of valuable medical knowledge. At present, the research on extraction of entity relationship in medical literature has made great progress, but with the exponential increase in the number of medical literature, the annotation of medical text has become a big problem. In order to solve the problem of manual annotation time such as consuming and heavy workload, a remote monitoring annotation method is proposed, but this method will introduce a lot of noise. In this paper, a novel neural network structure based on convolutional neural network is proposed, which can solve a large number of noise problems. The model can use the multi-window convolutional neural network to automatically extract sentence features. After the sentence vectors are obtained, the sentences that are effective to the real relationship are selected through the attention mechanism. In particular, an entity type (ET) embedding method is proposed for relationship classification by adding entity type characteristics. The attention mechanism at sentence level is proposed for relation extraction in allusion to the unavoidable labeling errors in training texts. We conducted an experiment using 968 medical references on diabetes, and the results showed that compared with the baseline model, the present model achieved good results in the medical literature, and F1-score reached 93.15%. Finally, the extracted 11 types of relationships were stored as triples, and these triples were used to create a medical map of complex relationships with 33 347 nodes and 43 686 relationship edges. Experimental results show that the algorithm used in this paper is superior to the optimal reference system for relationship extraction.医学文献含有丰富的有价值的医学知识。目前，在医学文献上的实体关系提取研究已经得到了很大的进步，但是随着医学文献数量以指数形式增加，医学文本的标注工作成为一个很大的问题。为解决人工标注耗时长、工作量大的问题，研究者提出了远程监督标注的方法，但这种方法会引入大量噪声。本文提出了一种基于卷积神经网络的新型神经网络结构，可以解决大量噪声问题。该模型可以利用多窗口卷积神经网络自动提取句子特征，在得到句子向量后，通过注意力机制选择对真实关系有效的句子。特别地，提出实体类型（ET）嵌入方法，通过加入实体类型特征用于关系分类。我们针对训练文本存在不可避免的标注错误问题，提出句子级别的注意力机制用于关系提取。使用 968 份糖尿病医学文献进行实验，结果表明，与基线模型相比，本文模型在医学文献中得到了较好的效果，F1 分数达到 93.15%。最后，我们将提取的 11 类关系以三元组的形式存储，并利用这些三元组制成具有 33 347 个节点、43 686 条关系边的复杂关系医学知识图谱。实验结果证明，本文所使用的算法明显优于用于关系提取的最佳基准系统。.
Aplikasi Web Registrasi Advertising Menggunakan Metode Paid-To-Promote

The use of websites for advertising activities is rarely found in Indonesia. Most of the websites developed are still for commercial purposes and some are free but limit certain facilities. This study aims to develop a website that can facilitate these needs. The website that is built is a website that can be used to help display advertisements from advertisers and provide opportunities for people who want to help researchers in serving advertisements with certain benefits. The system will pay members who promote ads even if they only see the ad. Website development begins with designing a website database. The next stage is designing a website using a codeigniter framework. This web development model uses the concept of model, view, and controller (MVC) in codeigniter. Website model development is becoming faster and more structured. The results of this web design research are used for community social activities to earn income via the internet.
Extra special issue on Soft/Social/Systemic (3S) Robot Technologies for enhancing Quality of New Normal (QoNN)

We are currently facing a situation where we need to adapt to the new normal for acting against the COVID19 pandemic. Soft/Social/Systemic (3S) Robot Technologies have great potentials for enhancin...
EFFECTIVENESS OF SCHOOL OPERATIONAL ASSISTANCE (BOS) MANAGEMENT IN MTS ALKHAIRAAT GORONTALO CITY

&lt;p&gt;This research confers the description of the effectiveness of School Operational Assistance (BOS) management in MTs Alkhairaat Gorontalo City and proponent and inhibiting factors. It used a qualitative descriptive approach. The research data sources were primary and secondary, collected with observation, interview, and documentation. The data analysis technique used was Miles and Huberman’s interactive analysis model. Findings demonstrate that (1) Because of its success, the School Operational Assistance program implemented in MTs Alkhairaat Gorontalo City was considered effective, (2) BOS management was ineffective, attested by stagnant students’ academic and non-academic achievements, (3) In general, the satisfaction level with the BOS program was effective, justified by parents’ responses arguing their satisfaction, (4) Input and output levels were still ineffective for there were many challenges which should be confronted to attain the program aims and targets, and (5) A successful BOS program implementation had a direct impact on the community, which now found it easy to fulfill education for children. Additionally, it is foreseeable that BOS can elevate students’ achievement in MTs Alkhairaat Gorontalo City.&lt;/p&gt;
Near-Optimal Data Source Selection for Bayesian Learning

We study a fundamental problem in Bayesian learning, where the goal is to select a set of data sources with minimum cost while achieving a certain learning performance based on the data streams provided by the selected data sources. First, we show that the data source selection problem for Bayesian learning is NP-hard. We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees. Next, we propose a fast greedy algorithm that improves the running times of the standard greedy algorithm, while achieving performance guarantees that are comparable to those of the standard greedy algorithm. The fast greedy algorithm can also be applied to solve the general submodular set covering problem with performance guarantees. Finally, we validate the theoretical results using numerical examples, and show that the greedy algorithms work well in practice.
Decision Support Portal for Access Certification

Recommendation system is a technology which makes fast and intelligent business decisions. The goal of this paper is to present a recommender system that makes such decisions about user certifications in an organizational environment. It aims at analyzing the user database of a company in order to recommend how many privileges should be removed or assigned to the user based on the existing privileges as well as actions taken on them. Two types of recommendations have been explored, namely Static recommendations and interactive recommendations. The major difference between them is that static recommendation works on the entire existing data of an organization, users and their existing entitlements. It analyses that data and gives recommendations if any user has excessive or missing entitlements compared to his peers. On the other hand, interactive recommendation gives recommendation for dynamic data. When any action such as addition or removal is taken on an existing entitlement, the system analyzes a user’s current assignment and recommends addition or removal of entitlements similar to the ones on which action was taken. Outlier detection techniques and Associative rule mining are used respectively in order to build the system and tackle the problem of misuse of privileges in an enterprise setting.
Responsible Prediction Making of COVID-19 Mortality (Student Abstract)

For high-stakes prediction making, the Responsible Artificial Intelligence (RAI) is more important than ever. It builds upon Explainable Artificial Intelligence (XAI) to advance the efforts in providing fairness, model explainability, and accountability to the AI systems. During the literature review of COVID-19 related prognosis and diagnosis, we found out that most of the predictive models are not faithful to the RAI principles, which can lead to biassed results and wrong reasoning. To solve this problem, we show how novel XAI techniques boost transparency, reproducibility and quality of models.
Strategy for the Effectiveness of the Implementation of Academic Information Systems in College in Efforts to Improve Information Quality

Information systems research success has begun to decline in the past few years, but existing research is still inaccurate in its definition and in the factors that involve the information system success. This research has designed to analyze, study, and find out the performance from implementing academic information systems in universities in an effort to improve the quality of information. The research was conducted using a qualitative approach and case studies, namely problem formulation, study proposition, analysis unit, data linkage to the proposition, the criteria for interpreting the findings and making conclusions and recommendations. From the results of the research data analysis, several conclusions were obtained, namely, the academic information system running in universities has not met expectations. The performance of academic information systems that can support the smooth operation of the universities is not well understood. It is necessary to formulate an optimal academic information system strengthening strategy that can support the smooth operation of the universities.
Transfer of Secret Data using Re-encryption Technique with Hyperledger Fabric based on Blockchain Technology

The Personal health record system (PHR system) which stores health-records patient's information. PHR system allows the one Hospital to manage and share his/her data with selected other individuals. The originality or tamper resistance feature is crucial for PHR system because it contains sensitive information about patients. Blockchain technology with the personal blockchain becomes a potential, great solution due to its immutability properties. Unfortunately. This work aims to propose a blockchain-based PHR model. The proposed model is built using the Hyper-ledger Fabric concept to support a tamper resistance feature. Re-encryption and other cryptographic techniques Such as Advanced Encryption Standard and Hashing algorithms are employed to preserve privacy. The proposed model include flexible access control, security concerns, auditability. A detailed security analysis of our model shows that the it is provably secure for Security and privacy preserving.
Biomedical Interpretable Entity Representations

Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.This can be a barrier to model uptake in important domains such as biomedicine.There has been recent work on general interpretable representation learning (Onoe and Durrett, 2020), but these domain-agnostic representations do not readily transfer to the important domain of biomedicine.In this paper, we create a new entity type system and training set from a large corpus of biomedical texts by mapping entities to concepts in a medical ontology, and from these to Wikipedia pages whose categories are our types.From this mapping we derive Biomedical Interpretable Entity Representations (BIERs), in which dimensions correspond to fine-grained entity types, and values are predicted probabilities that a given entity is of the corresponding type.We propose a novel method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging.We show that BIERs achieve strong performance in biomedical tasks including named entity disambiguation and entity label classification, and we provide error analysis to highlight the utility of their interpretability, particularly in low-supervision settings.Finally, we provide our induced 68K biomedical type system, the corresponding 37 million triples of derived data used to train BIER models and our best performing model.
Unsur Pembangun dalam Sastra Anak Novel Mata di Tanah Melus Karya Okky Madasari: Kajian Strukturalisme

Children's literature produced in Indonesia it self is not too much and studies are rarely carried out. Therefore, it is important to have an assessment of children's literature, especially novels. The research objective was to describe the building blocks in Okky Madasari's Mata di Tanah Melus novel. The approach used in this research is a structuralism approach. The research method used content analysis method. The data source is the novel Mata di Tanah Melus by Okky Madasari. The research time was carried out for one month. Data collection techniques using library techniques. The data collection instrument was the novel Mata di Tanah Melus by Okky Madasari. The data validity technique uses credibility testing techniques, namely increasing persistence and using reference materials. Data analysis in this study used Miles and Huberman's analysis model, namely data collection, data reduction, data presentation, and conclusions. The results showed that the building blocks found in the novel Mata di Tanah Melus, namely the facts of the story in the form of a forward plot. The main character is Matara, the supporting character consists of 18 people, the white character consists of 5 people, and the black character is the Hunters. The setting consists of 17 places. The time setting occurs in the morning, noon, and night. The socio-cultural background raises the culture of the Melus Tribe. The theme raised in the novel Mata di Tanah Melus is the theme of humanity. The means of the story are titles and points of view. The title of the novel contains two meanings and experiences semantic distortion. The point of view used is the main actor's first person point of view
A COMPARATIVE STUDY ON THE EFFECT OF USING PQ4R AND ETR STRATEGIES ON STUDENTS’ READING COMPREHENSION AT SMP PLUS TERPADU PEKANBARU

The aim of this study is to investigate the comparison of Preview, Question, Read, Reflect, Recite and Review (PQ4R) and Experience-Text-Relationship (ETR) strategies on Students’ Reading Comprehension. The research design was quasi-experimental by comparing two strategies, and two experimental groups with pre-test and post-test design. This research involves 64 participants which consisted of 32 students from experimental group 1 and experimental group 2 at SMP Plus Terpadu Pekanbaru. There were 3 research questions with 3 hypothesis proposed in this research in order to identify the significant difference between the effect of using PQ4R and ETR Strategies on students’ reading comprehension. Pre-test and post-test were administered in this study to two experimental groups. Independent sample t-test and paired sample t-test were used to analyzed the data. he research finding indicated that there was no significant difference of post-test mean score between an experimental group 1 which was taught by using PQ4R and ETR Strategies on students’ reading comprehension. The contribution of PQ4R strategy to improve students’ reading comprehension was 83% while ETR strategy was 85%. Finally, it can be concluded that both PQ4R and ETR Strategies can be implemented in improving Students’ Reading Comprehension at SMP Plus Terpadu PekanbaruKeyword: Reading Comprehension, PQ4R Strategy, ETR Strategy.
Randomized Generation of Adversary-aware Fake Knowledge Graphs to Combat Intellectual Property Theft

Knowledge Graphs (KGs) can be used to store information about software design, biomedical designs, and financial information---all domains where intellectual property and/or specialized knowledge must be kept confidential. Moreover, KGs can also be used to represent the content of technical documents. In order to deter theft of intellectual property via cyber-attacks, we consider the following problem: given a KG K0 (e.g., representing a software or biomedical device design or the content of a technical document), can we automatically generate a set of KGs that are similar enough to K0 (so they are hard to discern as synthetic) but sufficiently different (so as to be wrong)? If this is possible, then we will be one step closer to automatically generating fake KGs that an adversary has difficulty distinguishing from the original. We will also be closer to automatically generating documents corresponding to fake KGs so that an adversary who steals such documents has difficulty distinguishing the real from the fakes. We formally define this problem and prove that it is NP-hard. We show that obvious approaches to solving this problem do not satisfy a novel concept of "adversary-awareness" that we define. We provide a graph-theoretic characterization of the problem and leverage it to devise an "adversary-aware" algorithm. We validate the efficacy of our algorithm on 3 diverse real-world datasets, showing that it achieves high levels of deception.
Diagnostic Accuracy of Ultrasound in the Detection of Malignant Focal Solid Breast Lesions Taking Biopsy as a Gold Standard

Background: Now a day’s breast cancer is one of the most common causes of deaths and the most malignant condition among women. Objective: To determine diagnostic accuracy of ultrasound in detection of malignant focal solid breast lesions taking biopsy as a gold standard. Material and methods: This Cross-sectional analytical study was conducted at the department of radiology and pathology of Life Line Hospital. Duration of the study was 9 months after the approval of synopsis. Ultrasound system (Toshiba Xario) with linear transducer, frequency ranging 7 to 14 MHZ. All 78 participants were scanned while observing international standards of American institute of ultrasound in medicine (AIUM) guidelines for breast sonography. Results: Total 78 females with solid breast lesions were included in this study, out of them 28 (35.90%) were found in right while 50(64.10%) in left side. The distribution of breast lesions in Upper outer quadrant, Lower outer quadrant, Upper inner quadrant, and Lower inner quadrant were 50 (64.10%), 45 (57.49%), 13 (16.67%), 16 (20.51%) and 4 (51.13%) respectively. Mean of the depth to width D/W ration in malignant was 0.78±0.098 (0.42-0.93), while in benign was 0.52±0.141(0.35-0.85). there was a significant difference between the D/W ratio of benign and malignant solid breast lesion. The specificity, sensitivity, positive predictive value, negative predictive value, and diagnostic accuracy of ultrasound while taking histopathology as gold standard were 94.74%, 95.24%, 98.18%, 86.96%, and 94.87% respectively. Conclusion: Ultrasound can differentiate benign and malignant solid breast lesion to a great extent. With the application of this sonographic criteria in the cases of solid breast lesion before biopsy for histologic examination the number of unwanted biopsies will be reduced to a great extent.
LEADERSHIP AND CONNECTIVISM IN THE ACADEMIC ENVIRONMENT OF UNIVERSITIES

The article is devoted to the consideration of the problems of educational leadership in the academic environment. The new trend is aimed at the connectivist development of the structure of interaction between members of the organization, which creates advantages for the development of new scientific and innovative products of the university. The aim of the research is to analyze management styles and leadership in higher education. On the basis of a questionnaire, a test survey and diagnostics of situational-personal orientations, personal qualities and management styles, as well as the conditions for the formation of leadership and leadership potentials of leaders of different levels of management of a national university were analyzed. Shown are effective management methods at different structural levels of the university and considered the possibilities for proactive actions, which are owned by the leaders of the leading university with further extension to other universities. It has been established that implementation of the managerial leadership principle is an important element in improving the efficiency of managers' activities, therefore, effective management of the university and ensuring the quality of education in general. It is shown that the approach to stimulating effective management in Kazakhstani universities is underdeveloped. Along with this, it should be noted that development of the collective potential of leadership groups and educational leadership is limited. This all requires further discussion, deep analysis and appropriate measures. Key words: leadership, leader, educational leadership, managerial leadership, higher education, university, academic environment.&nbsp
Semi-Supervised Learning via Triplet Network Based Active Learning (Student Abstract)

In recent years, deep learning models have pushed state-of-the-art accuracies for several machine learning tasks. However, such models require a large amount of (supervised) data for training. While unlabelled data is available in abundance, manually labeling them is very costly. Active learning techniques helps in utilizing unlabelled data which may result in an improved classification model. In this research, we present an active learning algorithm which can help in increasing performance of deep learning models by using large amount of unlabelled data. A novel active learning algorithm, Triplet AL is proposed which uses a triplet network to select samples from an unlabelled data set. Previous active learning methods rely on classification model's final prediction scores as a measure of confidence for an unlabelled sample. We propose a more reliable confidence measure, termed as Top-Two-Margin which is given by the Triplet Network. The proposed algorithm shows improved performance compared to other active learning approaches.
Translating Booster Expressions: How the Competence of the Translator is Influential?

This research aims to see how booster expression as rhetorical devices in several presentation transcripts provided TED.com are translated from English into Indonesian. Given that there are cultural differences in the use of booster expressions from the two countries. Therefore, the objectives of this study are threefold. 1. To find out what presenters use booster expressions as rhetoric tools in several presentations at TED.com, 2. Then look at what translators use translation techniques in translating booster expressions from English into Indonesian. This research is descriptive qualitative research. Data were taken from six presentation transcripts at TED.com and transcripts translated into Indonesian. All data will be categorized according to the rhetorical function of the booster expression. Then the researcher will see what translation techniques are used in translating the booster expression. Data validation was carried out through FGD before the analysis was carried out. The study results revealed that not all the booster expressions could be well translated. This can be seen from the considerable number of deletion techniques applied by the translators. Several assumptions became the reason for selecting this technique, specifically; the cultural differences between the source language and the target language in using booster expressions and the lack of translator competence in translating booster expressions.