Reviewerly/exHarmony huggingface dataset:
Sanity Checks for Explanations of Deep Neural Networks Predictions

At the dawn of the fourth industrial revolution, the performance of Artificial Intelligence (AI) systems is reaching, or even exceeding, the human level on an increasing number of complex tasks. However, because of their nested non-linear structure, deep learning models often suffer from opacity and turn out to be uninterpretable black boxes. The lack of transparency represents a barrier to the adoption of these systems for those tasks where interpretability is essential, like autonomous driving, medical applications or finance. To overcome this drawback and to comply with the General Data Protection Regulation (GDPR), the development of algorithms for visualizing, explaining and interpreting deep neural networks predictions has recently attracted increasing attention. Paradigms underlying this problem fall within the so-called Explainable Artificial Intelligence (XAI) field. This class comprises a suite of methods and algorithms enabling humans to understand, trust and effectively manage the emerging generation of artificial intelligent partners. Over a relatively short period of time a plethora of explanation methods and strategies have come into existence, whose purpose is to highlight the regions of the input, typically an image, that are mostly responsible for reaching a certain prediction. However, despite the significant performances, it remains the difficulty of assessing the scope and quality of results provided by such explanation methods. The goal of this thesis is to validate the explanations of deep neural networks predictions for the computer vision, generated by some state-of-the-art methods recently proposed. The experiments conducted in this work aim to answer to the following question: who assures us that the explanation provided by the method actually tell us reliably about what the network has learned to arrive at that decision? Along this lines, a sanity check taken from the recent literature is described and experiments are performed to assess the sensitivity of explanation methods to model parameters. If one method really highlights the most important regions of the input, randomly reinitializing the parameters of the last layer of the network, then changing the output, the explanation given should change. Surprisingly, some of the methods proposed in literature are model independent and therefore fail the randomization test. By providing the same explanation even after the model parameter randomization, such methods are inadequate to faithfully explain the network prediction. The reliability of explanation methods is a crucial aspect in tasks where visual inspection of results is not easy or the costs of incorrect attribution is high. The analysis conducted in this thesis aims to provide useful insights into developing better and more reliable visualization methods for deep neural networks, in order to gain the trust of even the most skeptical users.
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Teaching Speaking Report Text through Documentary Video

Speaking is a part of English proficiency that should be mastered by student who learn second language, because the success of mastering second language is measured by the ability of carry out a conversation in form of language. The objective of this research was to prove whether or not the implementation of documentary video can improve the students’ speaking ability. This research was carried out on November 19th up 28th 2019 at SMA Negeri 08 Banda Aceh. This research applied an experimental research design. The research population was eleventh grade students of SMA Negeri 08 Banda Aceh. The sample of this research was selected by using purposive sampling. The instrument of data collection was test which was distributed to the students as pre-test and post-test. The researcher used a statistical analysis to analyze the data. The data were analyzed statistically in order to measure the difference in the achievement of the students in pre-test and post-test. The hypothesis result by using t-test showed that the obtained t-score was higher than t-table (tscore=54.9>ttable=1.703), the difference is statistically significant. Therefore, based on the result there is significant improvement before given treatment using documentary video and after treatment. Thus, it can be concluded that documentary video can improve students’ speaking skills and hence Ha is accepted because after  given treatment by documentary video, the students had better achievement in speaking skill.
Comparison of Explanatory Orientation and Experience Orientation for Intermaxillary Fixation in Nursing of Orthognathic Surgery Patients

In the field of nursing, we have been performing a presurgical verbal orientation using an explanation sheet for patients. However, many patients complained after surgery, “It was more stressful than I expected” and “It was hard to breathe.” These complaints indicate that patients suffered considerable stress by intermaxillary wire fixation. We thought that a presurgical orientation including a simulated experience would help them understand the postsurgical condition and relieve the postsurgical stress. Thus, we decided to perform a presurgical verbal orientation using either an explanation sheet （explanation group） or a simulated experience of using intermaxillary elastics （simulated-experience group） randomly assigned to patients, and compared the effect of the two methods. The results showed that both methods could provide enough information on intermaxillary wire fixation before surgery. Among the explanation group, there were many who replied that the surgery was the same as the image they had imagined before the surgery. In contrast, among the simulated-experience group, there were some who replied that the surgery was more stressful than expected. This was because the simulated experience using intermaxillary elastics, which could allow mouth opening, might have provided an incorrect image that intermaxillary wire fixation was not so stressful. Patients in the simulated-experience group felt that intermaxillary wire fixation was much more stressful than they had expected, and the orientation did not help relieve their stress. However, there were some who said the simulated experience was worthwhile and effective for orthognathic surgery. The present study highlighted the problems in performing our simulated experience, and enabled us to improve the method for pain relief.
Immunoinformatics approach for epitope-based diagnosis of hemorraghic diseases caused by arenaviruses

Introduction: Arenaviruses are rodent-borne pathogens that are important causes of hemorrhagic fever (HF) in Africa and South America.Human to human transmission plays a larger role in certain arenaviruses related outbreaks, for Lassa fever is estimated to cause up to 300,000 cases and 5,000 deaths per year in endemic regions of West Africa.HF cases have recently reemerged in Bolivia and Brazil with a high fatality rate, and several followups for close contacts.Although arenavirus infections have been limited to certain geographic areas, they pose as a serious challenge for local control of human cases and rodent reservoirs raise serious concerns about the potential for larger outbreaks in the future.Since arenaviruses are among the national compulsory notifiable diseases list and demand a biosafety level 4 for handling human samples, in this work we combined different in silico approaches to identify B-cell conserved regions on viral nucleoprotein of New World arenaviruses associated with hemorrhagic fever.Altogether, the results presented here could be used for new serologic diagnostic assays.Objective: Identify B-cell linear immunogenic epitopes on viral nucleoprotein conserved among New World arenaviruses associated with hemorrhagic fever. Methodology:In silico analysis were performed using the protein sequences of New World arenaviruses nucleoprotein (ANP) associated to hemorrhagic disease (Sabiá, Chapare, Machupo, Guanarito and Junín) obtained from NCBI.To predict immune antibody response targets, several protein features were assessed, including: surface exposure, flexibility, hydrophilicity, antigenicity and epitope location in protein quaternary structure.Linear Bcell epitopes were compared between: New World arenaviruses associated and nonassociated to hemorraghic diseases (Amapari, Aporé, Cupixi and Tacaribe) and Old World arenaviruses (Lassa and Lujo).Sabia NP ab initio homology modeling was carried out using Robbeta server.The best predicted models were chosen and the lowest energy one was selected.Resulting 3D structures were evaluated with Verify 3D and MolProbity. Results:In silico analyses identified five potential B-cell linear and immunogenic targets on ANP (ANP-1 to -5).ANP-4 epitope shares a high degree of sequence conservation (>90%) among all New World arenaviruses (associated or not) to hemorrhagic diseases and an intermediate similarity (50<x<65%) between Old World hemorrhagic arenaviruses.ANP-3 presents high degree of sequence conservation with Brazilian and Bolivian arenavirusesassociated to hemorrhagic diseases. Conclusion:Immunoinformatics approach identified antibody targets that could be used for diagnosis of New World hemorrhagic arenaviruses.
Influence of Users' Personality Traits on Psychological Stress Relief Effects by Robots' Different Reactions to Approach from Users

Considering that robot-assisted activities spread to ordinary homes, this study was conducted as a part of acquiring the information on the relationship between users' characteristics, reactions from a robot, and the effects of those reactions. I compared the influence of users' personality traits on psychological stress relief effects obtained by interacting with tolerant, rejective, and emotional reaction robots to approach from users. As a result, I confirmed that agreeableness and neuroticism personality traits have significant influences on relaxing the fatigue obtained by tolerant reactions from a robot and disarming the anger obtained by emotional reactions from a robot, respectively. I also confirmed that improving the vigor obtained by rejective reactions from a robot could not be expected independently of users' personality traits. These results suggest that it should avoid using the robot with a high ratio of rejective reactions in any robot-assisted activities. Besides, it is better to use the robot with a high ratio of tolerant reactions if a user with high agreeableness needs effects of relaxing the fatigue, and use the robot with a high ratio of emotional reactions if a user with high neuroticism needs effects of disarming the anger.
Fasting Glucose Identification Using Machine Learning: A Fundamental Step in Automated Diabetes Phenotyping in Electronic Medical Records

Background: The fasting blood glucose (FBG) values extracted from electronic medical records (EMR) are typically assumed to be valid in existing research, which may cause diagnostic bias. We proposed a machine learning (ML) algorithm to predict the fasting status of blood samples.Methods: We evaluated the distribution of BG values in the China Medical University Hospital Clinical Research Data Repository and established the theoretical fasting status when the ontologically labeled FBG was lower than the estimated average glucose calculated from HbA1c. In addition to logistic regression, we extracted 67 features to predict the fasting status by eXtreme Gradient Boosting (XGBoost). The discrimination and calibration of the prediction models were also assessed.Findings: Of the 784340 ontologically labeled fasting samples, 77·1% were considered theoretical FBGs. The median (IQR) glucose and HbA1c level of ontological and theoretical fasting samples in patients without diabetes mellitus (DM) were 110 mg/dL and 5·6%, and 92 mg/dL and 5·6%, respectively. In multiple logistic regression, 14 selected variables were significantly associated with the fasting status, but the area under the receiver operating characteristic curve (AUROC) was only 0·868. XGBoost showed better calibration and achieved an AUROC of 0·892 with the top five contributing predictors of glucose level, distance from home to hospital, age, height, and concomitantly prescribed serum creatinine.Interpretation: Our results demonstrated an innovative approach to clean EMR data and detect true FBG, which could aid more accurate estimation of the global and local epidemiology of DM.Funding: This study was supported by the Ministry of Science and Technology of Taiwan (grant number: 108-2314-B-039-038-MY3 and 109-2321-B-468-001)Declaration of Interest: None to declare. Ethical Approval: The study was approved by the Research Ethical Committee/Institutional Review Board of China Medical University Hospital (CMUH105-REC3-068).
RuSentEval: Linguistic Source, Encoder Force!

The success of pre-trained transformer language models has brought a great deal of interest on how these models work, and what they learn about language. However, prior research in the field is mainly devoted to English, and little is known regarding other languages. To this end, we introduce RuSentEval, an enhanced set of 14 probing tasks for Russian, including ones that have not been explored yet. We apply a combination of complementary probing methods to explore the distribution of various linguistic properties in five multilingual transformers for two typologically contrasting languages – Russian and English. Our results provide intriguing findings that contradict the common understanding of how linguistic knowledge is represented, and demonstrate that some properties are learned in a similar manner despite the language differences.
Research on Entity Label Value Assignment Method in Knowledge Graph

The lack of entity label values is one of the problems faced by the application of Knowledge Graph. The method of automatically assigning entity label values still has shortcomings, such as costing more resources during training, leading to inaccurate label value assignment because of lacking entity semantics. In this paper, oriented to domain-specific Knowledge Graph, based on the situation that the initial entity label values of all triples are completely unknown, an Entity Label Value Assignment Method (ELVAM) based on external resources and entropy is proposed. ELVAM first constructs a Relationship Triples Cluster according to the relationship type, and randomly extracts the triples data from each cluster to form a Relationship Triples Subset; then collects the extended semantic text of the entities in the subset from the external resources to obtain nouns. Information Entropy and Conditional Entropy of the nouns are calculated through Ontology Category Hierarchy Graph, so as to obtain the entity label value with moderate granularity. Finally, the Label Triples Pattern of each Relationship Triples Cluster is summarized, and the corresponding entity is assigned the label value according to the pattern. The experimental results verify the effectiveness of ELVAM in assigning entity label values in Knowledge Graph.
Dynamics of Digital Finance and Financial Inclusion Nexusin Sub-Saharan Africa

With the revolution in the financial technology space occasioned by competition among financial marketintermediaries, there is no doubt that more unbanked and under-banked citizens will be captured into the financialnet of the economy. This study examined the dynamic relationship between digital finance and financial inclusion in27 sub-Saharan African countries. Granger Error Correction Method (ECM) with General Methods of Moments(GMM) of Arellanon and Bond (1991) were used to analyze the short panel data. The study found that a positivelong-run relationship exists between digital finance and financial inclusion. It therefore recommends amongst othersthat monetary authorities of emerging and developing economies in sub-Sahara African countries should embracedigital financial technologies by encouraging commercial banks to install more ATMs and discourage acceptance ofcash payment and withdrawals within established thresholds across bank counters in their respective countries
C-Test Collector: A Proficiency Testing Application to Collect Training Data for C-Tests.

We present the C-Test Collector, a web-based tool that allows language learners to test their proficiency level using c-tests. Our tool collects anonymized data on test performance, which allows teachers to gain insights into common error patterns. At the same time, it allows NLP researchers to collect training data for being able to generate c-test variants at the desired difficulty level.
Developing a model for teacher professional digital competence

Thinking Tomorrow's Education: Learning from the past, in the present and for the future - Aachen, Germany
PENERAPAN PEMBELAJARAN BERBASIS KOMPUTER (PBK) MODEL TUTORIAL DI SMA KRISTEN 1 KUPANG

ABSTRACKActivity and process study of TIK class student of X IPA-A SMA Christian 1 Mussel still lower. That thing is because of its minim of exploiting of media in study. Therefore, require to to strive repair in study of TIK, one of them by using media study of video of tutorial in course of study. This research aim to to increase the understanding of activity learn student so that affect at make-up of understanding learn TIK class student of X IPA-A SMA Christian 1 Mussel [pass/through] usage of video media of tutorial in study.
Reliability of Human Evaluation for Text Summarization: Lessons Learned and Challenges Ahead

Only a small portion of research papers with human evaluation for text summarization provide information about the participant demographics, task design, and experiment protocol. Additionally, many researchers use human evaluation as gold standard without questioning the reliability or investigating the factors that might affect the reliability of the human evaluation. As a result, there is a lack of best practices for reliable human summarization evaluation grounded by empirical evidence. To investigate human evaluation reliability, we conduct a series of human evaluation experiments, provide an overview of participant demographics, task design, experimental set-up and compare the results from different experiments. Based on our empirical analysis, we provide guidelines to ensure the reliability of expert and non-expert evaluations, and we determine the factors that might affect the reliability of the human evaluation.
Analisis User Interface pada Aplikasi Sumeks.co Menggunakan Metode System Usability Scale (SUS)

User interface design is one of the main problems that are often faced in the development of the sumeks.co mobile application. because there are several reasons about the sumeks.co application userinterface has a display that is not attractive both in terms of features or other appearance. For this reason, it is necessary to carry out further analysis of the sumeks.co application so that it can be seen whether the sumeks.co application has been successful or not in terms of its interface from the user side. The method used in this study is to use the System Usability Scale SUS is a method that can evaluate a system or application accurately and quickly with 10 statement items. The analysis results were obtained from 100 respondents who were distributed through questionnaires that were available by the SUS method. The result of the research obtained is that the sumeks.co application gets a SUS score of 65.39. On the acceptability ranges side, it is at the marginal high level, on the grade scale side it is grade D and the last is on the rating adjective side at OK position.
Person-Job Fit model based on sentence-level representation and theme-word graph

Previous studies on Person-Job Fit mainly focused on the use of large amounts of data to train word-level embedding to model with the Joint Feature Extractor. Therefore, in order to reduce the computational load and text truncation problems caused by word-level representation and better integrate the idea of Click-through Rate prediction, a Person-Job Fit model based on sentence vector and theme-words knowledge graph (KG-DPJF) is proposed. The model uses a Bi-directional Long Short-term Memory Neural Network to extract theme-words from long text of recruitment resume and requirements and construct a knowledge graph. The model uses the Bert pre-training model to code the input sentences, and uses a multi-level attention mechanism to calculate the correlation among candidate resumes, historical resumes and recruitment requirements features, then inputs the correlation and weighted output to the classifier to predict the matching degree. Finally, the experiment is tested in the industry data set, and the KG-DPJF method achieves nearly 9% and 5% performance improvement compared with method based on logistic regression and single convolution neural network feature extraction method, and shortens training time by about four times compared with word-level embedding method.
What to Do Before Choosing Any Online Assignment Help

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PARALLELIZATION OF TIM SORT ALGORITHM USING MPI AND CUDA

Tim Sort is a sorting algorithm developed in 2002 by Tim Peters. It is one of the secure engineeredalgorithms, and its high-level principle includes the sequence S is divided into monotonic runs (i.e., nonincreasingor non-decreasing subsequence of S), which should be sorted and should be combined pairwiseaccording to some specific rules. To interpret and examine the merging strategy (meaning that the order inwhich merge and merge runs are performed) of Tim Sort, we have implemented it in MPI and CUDAenvironment. Finally, it can be seen the difference in the execution time between serial Tim Sort and parallelTim sort run in O (n log n) time .
LibGuides: Career Research Project for Principles of Business Management & Entrepreneurship: Career Pathway Websites & Databases

This LibGuide will provide the necessary print and digital resources required to complete Ms. Stieg's Career Research Project in Principles of Business Management & Entrepreneurship.
Faktor-Faktor Yang Mempengaruhi Kesuksesan Implementasi Sistem Informasi Studi Kasus Sistem Informasi Akademik (SIAKAD) STKIP Muhammadiyah Pringsewu Lampung

This research used the success model of information system from Delone and Mc. Lean which has been modified based on the need and the situation at STKIP Muhammadiyah Pringsewu. The respondents were the first year students in the academic year 2012-2013 and the operators of SIAKAD. The number of the respondents were 196 students and 10 operators. The process of validity and reliability and the hyphotesa used Structural Equation Modeling (SEM). Which is analysed using Amos 20 software.The result of this research showed that from 5 hyphotesis which have been formulated, four of them were received. These hypothesis are the following : the quality of information influence the satisfying of the user ; the quality of the system influence the satisfying of the user; the trust influence the satisfying of the user; and the satisfying of the user influence the benefits of the SIAKAD. On the other hard, the quality of the service doesn’t has significant influence to the satisfying of the SIAKAD user. The success factors, which were resulted in this research, will be used as the suggestion and recomendation in the next SIAKAD development so that the sevice quality of SIAKAD to the students will increase. Keywords: Information Systems Success, DeLone and McLean Model, Structural Equation Modeling (SEM), Analysis of Moment Structures (AMOS).