Reviewerly/exHarmony huggingface dataset:
THE ROLE OF INFORMATION TECHNOLOGY IN ONLINE LEARNING DURING THE COVID-19 PANDEMIC OF UPT SDN 1 MARGAKAYA: indonesia

AbstractCovid-19 Pandemic was greaty impact on education in Indonesia. The learning process which was performed at the school but now learning do from home with online learning. Using of information technology is very helpful on learning process in the Covid-19 virus. Online based learning used in this study is learning that utilizes the use of Whatsapp in information communication tools. However, there are some obstacles in he process of implementing learning at UPT SDN 1 Margakaya such as not all students can take part in the online learning process because of the weak internet network and not all guardians of students have handphone andenthusiastic in participating in the online based learning process. This research was conducted to determine the role of information technology in online learning during Covid-19 Pandemic of UPT SDN 1 Margakaya. This research method uses qualitative methods with a descriptive approach. The result of this research is that the whatsapp application based learning has an important role in the implementation of learning, the other than that there are factors that greatly influence the learning process when carried out online during the Covid-19 Pandemic.Keywords : Covid-19 Pandemic, Online learning, Information Technology
Analysis of Students’ Interests, Motivations, and Obstacles to Online Learning During the Covid-19 Pandemic

This study raises the issue of how and whether there are differences in students' interests, motivations, and barriers to online learning during the covid-19 pandemic in Rikit Gaib High School Student and Pantan Cuaca High School Student. This study aims to find out how and whether there are differences in students' interests, motivations, and barriers to online learning during the covid-19 pandemic in Rikit Gaib High School Student and Pantan Cuaca High School Student. The benefits of this reearch are expected to provide secientific treasures and literacy in the field of education, epecially during this covid-19 pandemic mass and practical benefits for teacher to be able to find out how the minimum, student motivation and what are the learning obstacles during the covid-19 pandemi and students participate actively in the learning process.The subjects in this study were students Rikit Gaib High School Student and Rikit Gaib High School Student Pantan Cuaca of 50 people. The research approach used is descriptive qualitative. Data collection was carried out using a direct-based closed questionnaire. The data analysis technique used in this study is the percentage. Research result shows that the most dominant indicator of interest in learning is a sense of interest in online Rikit Gaib High School Student, less than half of students are interested in online learning, the most dominan indicator of learning motivasion is diligent in studying for High School Student Rikit Gaib more than half of students are diligent in learning online, and High School Student In Pantan Cuaca more than half students are diligent in learning online, And the most dominan indicator of learning obstacles is not being able to study properly in high school students Rikit Gaib more than half of the students can study properly and less than half of the students of High School Students Pantan Cuaca can study properly.
Educational Challenges in Pakistan: Facts or Falsity

This is a review type research focusing on educational problems in Pakistan. Fifteen related researches were sampled. Education lays the basis for every country's political, social, and economic growth. A sustainable school framework allows the country to accomplish national targets. As a developing world, Pakistan has faced crucial educational problems since its birth, so the education system has struggled to perform on the nation's expectations. Until now, schooling at any stage trains the individual for the next level until he brings the person to tertiary industry; therefore, there is a strong link with Pakistan's education and growth; obviously, the indicator doesn't reflect positive outcomes. There is an untrained teaching workforce with fluctuating enrollments and limited-resource facilitation, a dual educational medium, inadequate assessment mechanism, and government allocating low budgets to the education system. It was conluded that Pakistan has serious educational problems. Suggestions were made based on the findings. Moreover, there is a shortage of study that encourages innovative methods of handling decision-making, and gender inequality is often seen as an issue that cannot be ignored, so this subject is chosen as a domain to focus on and advice to address. Keywords: development, education, educational budget, educational problems, Pakistan
Academic literature recommendation technology based on two-layer attention network

Abstract Recently, academic literature recommendation for learners has become an important topic. Recently deep learning based models are used in literature recommendations, which follow a similar Embedding&amp;MLP paradigm. However, in the base model and its follow-up model Deep Interest Network (DIN), users only have to click or not click on items, limiting the expression of users' specific interests. Therefore, we propose a two-layer attention model (BIH) based on DIN. BIH adds specific behaviors to the user behavior sequence and adds a behavior attention layer, which can learn the expression of user interests more accurately. Experiments on a public dataset and users' behavior logs of academic literature demonstrate the effectiveness of proposed approaches, which achieve superior performance on both Macro-F1 and Micro-F1 compared with the base model and DIN.
Viewpoint Invariant Dense Matching for Visual Geolocalization.

In this paper we propose a novel method for image matching based on dense local features and tailored for visual geolocalization. Dense local features matching is robust against changes in illumination and occlusions, but not against viewpoint shifts which are a fundamental aspect of geolocalization. Our method, called GeoWarp, directly embeds invariance to viewpoint shifts in the process of extracting dense features. This is achieved via a trainable module which learns from the data an invariance that is meaningful for the task of recognizing places. We also devise a new self-supervised loss and two new weakly supervised losses to train this module using only unlabeled data and weak labels. GeoWarp is implemented efficiently as a re-ranking method that can be easily embedded into pre-existing visual geolocalization pipelines. Experimental validation on standard geolocalization benchmarks demonstrates that GeoWarp boosts the accuracy of state-of-the-art retrieval architectures. The code and trained models are available at this https URL
Succinct Euler-Tour Trees.

We show how a collection of Euler-tour trees for a forest on $n$ vertices can be stored in $2 n + o (n)$ bits such that simple queries take constant time, more complex queries take logarithmic time and updates take polylogarithmic amortized time.
Investigating how blockchain is transforming the shipping industry: a systematic literature review

Advanced technology provides greater opportunities for physical integration of transportation modes driven by containerization in respond to the evolving demands of consumer. The inception of Web 2.0, together with Industry 4.0, which introduced digitalisation including Blockchain technology has ushered in a substantial attention in shipping and port operation. The introduction of Blockchain technology - a distributed ledger server to store, verify, and authenticate parties' transactions - will increase the efficiency, consistency, and visibility of the entire supply chain system. It is expected that Blockchain will soon become a standard practice throughout the industry. Many scholars advocate that the next wave of technology will come from Blockchain as it represents the foundation of a new digital supply chain system. This will help improving the overall efficiency by defining traceability while enabling source tracking, optimizing order and freight tracking, and fostering trust between the organization and customers. Therefore, this paper through a comprehensive systematic literature review attempts to investigate the possibility of application of blockchain in shipping and port operation.
Low-Dose Dynamic CT Perfusion Denoising Without Training Data

Cerebral perfusion maps derived from low-dose computed tomography (CT) data typically suffer from low signal-to-noise ratio (SNR). Obtaining denoised perfusion maps is critical for improved quantitative accuracy and clinical decision making. Several prior works focus on denoising the low-dose CT (LD-CT) images followed by perfusion map generation via regularized deconvolution. Recently, supervised deep neural networks (DNN) have been employed for learning a mapping between the perfusion maps obtained at low-dose and corresponding maps obtained at standard dose. Supervised learning-based methods rely on large amount of training data for improved accuracy. However, they suffer from changes in acquisition protocol and risk missing patient specific information, which is critical for applications such as stroke imaging. In this work, we address the problem of handling changes in signal characteristics while retaining patient-specific information. For this purpose, we explore a combination of self-supervised and unsupervised deep neural networks to obtain denoised perfusion maps from low-dose noisy CT data. We propose a two-stage sequential approach for obtaining improved perfusion maps to leverage statistical independence of noise in the measurement space. First, we denoise the low-dose CT projection space data using a self-supervised method. Subsequently, we reconstruct the CT images from the denoised sinograms using fast reconstruction methods such as filtered backprojection. Second, we use the improved CT images as anatomical prior to refine the noisy CBF maps obtained directly from the LD-CT data. Through empirical experiments, we show that our model is robust both qualitatively and quantitatively to changes in signal characteristics of the acquired dynamic CT data.
E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks

Recently, there has been an increasing number of effortsto introduce models capable of generating natural languageexplanations (NLEs) for their predictions on vision-language(VL) tasks. Such models are appealing, because they can provide human-friendly and comprehensive explanations. However, there is a lack of comparison between existing methods,which is due to a lack of re-usable evaluation frameworksand a scarcity of datasets. In this work, we introduce eViL and e-SNLI-VE. e-ViL is a benchmark for explainablevision-language tasks that establishes a unified evaluationframework and provides the first comprehensive comparisonof existing approaches that generate NLEs for VL tasks. Itspans four models and three datasets and both automaticmetrics and human evaluation are used to assess modelgenerated explanations. e-SNLI-VE is currently the largestexisting VL dataset with NLEs (over 430k instances). We alsopropose a new model that combines UNITER [15], whichlearns joint embeddings of images and text, and GPT-2 [38],a pre-trained language model that is well-suited for text generation. It surpasses the previous state of the art by a largemargin across all datasets. Code and data are availablehere: https://github.com/maximek3/e-ViL.
PUBLIC SPEAKING TRAINING MODEL BASED ON LIFE AND CONTEXTUAL LEARNING FOR TEACHERS OF MI ALAM AL IKHLAS SENGGURUH KEPANJEN, MALANG

Speaking is the process of delivering information from one person to another as opposed to the person speaking. Speech activity is marked by the speaker and the person being spoken to, the purpose or purpose of speaking, the atmosphere of the conversation, the content of the conversation, and the manner or strategy of speaking. Speech activities are said to be successful if the information reaches the individual or the audience well. The information by the recipient is then understood, studied, applied according to the purpose of the message, developed as a new form of creativity.
OS BIOMARCADORES TAU, Aβ E NFL NA PROGRESSÃO DA DOENÇA DE ALZHEIMER

Alzheimer's disease is the most frequent cause of dementia, characterized by an irreversible neurodegenerative process that mainly affects the elderly. Diagnosis detected early, as well as knowledge about the progression of the disease course, favors the choice of more assertive treatments and, consequently, allows greater well-being and quality of life for affected patients. However, currently, the invasiveness and high costs for its diagnosis become another barrier, not only for clinical performance, but also for studies that may come to elucidate the aspects that are still unclear about this disease. Therefore, this study sought to investigate blood biomarkers capable of detecting the progression of Alzheimer's disease through an integrative literature review with the identification of relevant productions on the subject. Data collection was performed using PubMed and Science Direct databases with the analysis of studies published from 2016 to 2021, related to the biomarkers Tau, beta-amyloid and light chain neurofilament. Among them, 8 works were selected according to the inclusion criteria established in this study. Thus, it is concluded that although the findings presented in the studies are relevant, to date there are no highly accurate biomarkers for detecting the progression of Alzheimer's disease, and further studies are needed to ensure a clear and consistent diagnosis.
ANALYSIS AND LOCATION PREDICTIONOF THE DATA FROM THE TWITTER ACCOUNT USING MACHINE LEARNING

This Paper is mainly aiming to find the location from the geo –map which has been predicted from the twitter data. When the user who posted the tweet on twitter for various purposes like territory knowing or an illegal messages, it is necessary to know the location where the data has been posted. This will help to track and reduce the crime activites. For this activity KNN to find the location of the message and SVM to classify the message have been used. Location information will be in the form of country/city names with hashtags.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail. Counterfactual methods, on the other hand, indicate how to perturb the input to change the model prediction, providing details about the model's decision-making. Unfortunately, current counterfactual methods make ambiguous interpretations as they combine multiple biases of the model and the data in a single counterfactual interpretation of the model's decision. Moreover, these methods tend to generate trivial counterfactuals about the model's decision, as they often suggest to exaggerate or remove the presence of the attribute being classified. Trivial counterfactuals are usually not valuable, since the information they provide is often already known to the system's designer. In this work, we propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model's prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. We will make the code public.
Research Guides: Japanese Language Resources - Introduction for Graduate Students: Japanese Databases

This guide accompanies the library orientation session and offers a basic introduction to library research skills and tools for graduate students using Japanese language resources.
LibGuides: Cataloging Work Group: Other LSP Guides

LSP Cataloging Work Group Main Lib Guide- information on events, documentation, and resources A list of other LSP Guides to visit
LibGuides: Library Intranet: Circulation

To share teaching, learning and research support information Circulation is an integral part of the library. this page will provide relevant information pertaining to circulation matters
Pengetahuan Siswa tentang Penyalahgunaan Narkoba

This research examinesStudent Knowledge About Drug Abuse Among Students of SMA Negeri 1 Pendopo Barat, Pendopo Barat District, Empat Lawang Regency.This study aims to obtain information about students' knowledge of drug abuse among students at SMA Negeri 1 Pendopo Barat, Pendopo Barat District, Empat Lawang Regency. The type of research used is quantitative without testing the hypothesis. And to collect information using the saturated sample technique by giving questionnaires to 93 students of SMA Negeri 1 Pendopo Barat, Pendopo Barat District, Empat Lawang Regency.The results of this study explain that the 6 (six) sub-indicators that exist with the average percentage per sub-indicator are impaired brain function and normal development, the results are yes to know (very good), intoxication (poisoning) is yes to know results ( very good), it is difficult to control oneself there are results of not knowing at all (not good), health problems have results of yes knowing (very good), negative attitudes in society have results of not knowing (quite good) and weak education there are results of yes knowing ( very good). Thus, the results of the analysis of all the research sub-indicators showed good results, and only one indicator, namely the difficulty of controlling oneself which was not good. This shows that the students of SMA Negeri 1 Pendopo Barat, Pendopo Barat District, Empat Lawang Regency, have known the dangers of drug use.
Keep CALM and Improve Visual Feature Attribution

The class activation mapping, or CAM, has been the cornerstone of feature attribution methods for multiple vision tasks. Its simplicity and effectiveness have led to wide applications in the explanation of visual predictions and weakly-supervised localization tasks. However, CAM has its own shortcomings. The computation of attribution maps relies on ad-hoc calibration steps that are not part of the training computational graph, making it difficult for us to understand the real meaning of the attribution values. In this paper, we improve CAM by explicitly incorporating a latent variable encoding the location of the cue for recognition in the formulation, thereby subsuming the attribution map into the training computational graph. The resulting model, class activation latent mapping, or CALM, is trained with the expectation-maximization algorithm. Our experiments show that CALM identifies discriminative attributes for image classifiers more accurately than CAM and other visual attribution baselines. CALM also shows performance improvements over prior arts on the weakly-supervised object localization benchmarks. Our code is available at this https URL.
“STUDY OF SERUM PROTEINS AND DNA PATTERN OF DIABETIC AND NON-DIABETIC SERUM SAMPLES WITH THROMBOLYTIC ACTIVITY OF STREPTOKINASE ENZYME”

ABSTRACTBackground: The 80% of patients with diabetes mellitus die a thrombotic death. A blood clot (thrombus) developed in the circulatory system can cause vascular blockage leading to serious consequences including death.Objectives: To estimate the serum protein study and DNA pattern in normal individual and diabetic patients by using following parameters, Protein profiling SDS PAGE (sodium dodecayl sulphate poly acryl amide gel electrophoresis),DNA study,PCR (polymerase chain reaction),RFLP (Restriction Fragment length polymorphism Secondary objectives were To find out the thrombolytic activity of streptokinase enzyme in DM patients presented with cardiovascular diseasesMaterials & methods: The study was carried out in 11samples of Diabetic mellitus patients. Samples were collected and processed in centre for Bioscience and Nanoscience Research the within a period of 2 months (October 2015– December 2015). The samples were collected & processed by standard biochemical technique.such as comparative study of albumin / globulin (A/G) ratio,protein ,albumin,and globulin in DM and normal individual were done. Determination of molecular weight of protein by Sodium Dodecyl sulphate- Polyacrylamide Gel Electrophoresis.and DNA study also included,the study of streptokinase enzyme were done.Result: Eleven diabetic patients were selected for study. By using significant testing pattern used to achieve satisfactory results DNA was isolated and its pattern was studied using agarose gel electrophoresis. The serum protein pattern study was also conducted. Using RFLP method for separating this. SDS PAGE is a powerful technology that can detect and identify the changes of the structure and function of proteins in response to intra- and extracellular environmental signals or disease states. As proteomics (Proteomics is a large-scale, comprehensive study of the proteins of a cell or organism. It is a unique means of characterizing proteins that are expressed in a cell or tissue at any given time-point and of identifying any modifications that they may undergo.) Can establish a link for genes and proteins with a disease, it will play an important role in defining the molecular determinants of a disease and in identifying targets for drug discoveries and diagnostics. We have carried out proteomics studies in sample that are obtained from diabetic patient that are related to cardio vascular diseases and also investigate the significant fibrinolytic role of streptokinase enzyme in cardio vascular disease.
Conversão Texto-Fala para o Português Brasileiro Utilizando Tacotron 2 com Vocoder Griffin-Lim

This paper presents the training of a state-of-theart neural network model, Tacotron-2, using a open-source voice dataset from the Common Voice project.Results from training the model from scratch and by applying transfer learning of a pre-trained english model were evaluated.The results show that it is possible to train the model with limited data resources.