Reviewerly/exHarmony huggingface dataset:
IDENTIFICATION OF COMPONENTS FOR THE EVALUATION OF FUNDED RESEARCH PROJECT: A SYSTEMATIC LITERATURE REVIEW

The purpose of this paper is to identify different components that can help to determine the effect of study findings and to identify different methodologies as well. To clarify the extent of the study on the impact assessment of funded initiatives in various countries, a systematic literature review is carried out. Methodology: It is important to consider the effects of scientific findings to achieve value for the resources spent in science and technology. A systematic literature review (SLR) is carried out to gain a detailed understanding of the different literature available in the area of assessment of the search results of the projects funded. A total of 72 papers are collected from Google scholars to perform the SRL, which are screened according to the requirements for inclusion and exclusion. Further 20 papers, primarily focusing on two study fields, namely the academic and health care industries, are accepted. The 20 papers chosen are mapped accordingly based on the market, country of origin, variables, and approaches used by different authors. Conclusion: To determine the most significant variables commonly used by numerous writers in their studies, a systematic literature review is undertaken. The methodologies used by writers in their studies are also identified in this study. This paper can also be used by other researchers to describe in their studies the components and methodologies commonly used. Originality/Value: The current research assists policymakers in making decisions on which components to examine when assessing funded initiatives that improve the effects, quality, and productivity in the higher educational sectors, and there has been relatively limited research in this field.
Progressive Coordinate Transforms for Monocular 3D Object Detection

Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment. While significant progress has been achieved with expensive LiDAR point clouds, it poses a great challenge for 3D object detection given only a monocular image. While there exist different alternatives for tackling this problem, it is found that they are either equipped with heavy networks to fuse RGB and depth information or empirically ineffective to process millions of pseudo-LiDAR points. With in-depth examination, we realize that these limitations are rooted in inaccurate object localization. In this paper, we propose a novel and lightweight approach, dubbed {\em Progressive Coordinate Transforms} (PCT) to facilitate learning coordinate representations. Specifically, a localization boosting mechanism with confidence-aware loss is introduced to progressively refine the localization prediction. In addition, semantic image representation is also exploited to compensate for the usage of patch proposals. Despite being lightweight and simple, our strategy leads to superior improvements on the KITTI and Waymo Open Dataset monocular 3D detection benchmarks. At the same time, our proposed PCT shows great generalization to most coordinate-based 3D detection frameworks. The code is available at: this https URL .
The Connection between Out-of-Distribution Generalization and Privacy of ML Models

With the goal of generalizing to out-of-distribution (OOD) data, recent domain generalization methods aim to learn feature representations whose effect on the output remains invariant across domains. Given the theoretical connection between generalization and privacy, we ask whether better OOD generalization leads to better privacy for machine learning models, where privacy is measured through robustness to membership inference (MI) attacks. In general, we find that the relationship does not hold. Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashion-MNIST, and Chest X-rays, we show that a lower OOD generalization gap does not imply better robustness to MI attacks. Instead, privacy benefits are based on the extent to which a model captures the stable features. A model that captures stable features is more robust to MI attacks than models that exhibit better OOD generalization but do not learn stable features. Further, for the same provable differential privacy guarantees, a model that learns stable features provides higher utility as compared to others. Our results offer the first extensive empirical study connecting stable features and privacy, and also have a takeaway for the domain generalization community; MI attack can be used as a complementary metric to measure model quality.
PENGARUH PEMBERIAN OLES MADU TERHADAP PROSES PENYEMBUHAN LUKA DIABETIK DI WILAYAH KERJA PUSKESMAS LUBUK BUAYA PADANG

The incidence of Diabetes Mellitus in West Sumatra Province has increased by 1.3% (Rakerkesda, 2018). The health center with the highest incidence of Diabetes Mellitus is the Lubuk Buaya Health Center, which is 1312 people with diabetic wounds as many as 63 people (Pandang City Health Office, 2020). The purpose of this study was to determine the effect of applying honey on the healing process of diabetic wounds in the Lubuk Buaya Padang Working Area. The type of research used is a quasi-experimental study with a one group pretest-pottest design, this research was carried out in the Lubuk Buaya Padang Working Area from March to August 2021. The population was all respondents who had diabetic wounds as many as 15 samples were taken. . The sampling technique is purposive sampling. Data analysis with univariate analysis using mean and standard deviation and bivariate using t-test statistical test with 95% confidence level = 0.05 The results showed that the average value of autolysis before applying honey on diabetic wounds was 2.7 with a standard deviation of .45774. After being applied with honey, the average value of autolysis was 1.5 with a standard deviation of .51640. There is an effect of applying honey on the healing process of diabetic wounds with a p-value of 0.000. This research is expected to be considered and input for the head of the puskesmas to motivate health workers, especially in the nursing unit, in applying non-pharmacologicalGiving Honey Rule therapy to treat diabetic wounds.
ON DEMAND: Use the library as easily as you use your phone!

Our research focuses on two teaching challenges. Primarily, we seek to improve information literacy in undergraduate Mason students through an increased use of library databases and resources. Second, we are analyzing how the availability of mobile technology impacts students’ access and use of library resources. Our subjects for our research were a Mason Core course, Marketing Management 471. We surveyed all sections of this class from 2016-2020, including our final semester during the move to online, Spring 2020. We will share the main points of our surveys, including the impact of moving to an online environment in the middle of the semester. Our research questions are: 1) How do we manage and understand the ways that students utilize technology? 2) How does that affect their information literacy when doing library research for market plans?
Research Guides: The Sixth Extinction (First Year Read, 2019): About the Author

The 2019 selection of Stevens' First Year Reading Program. More on Elizabeth Kolbert, author of The Sixth Extinction.
Admonishment System for Human Physical Distancing Violators Using Faster Region-Based Convolutional Neural Network with Detectron2 Library

Physical distancing is essential to help prevent the spread of Coronavirus Disease 2019 (COVID-19). However, people often are unaware of their physical distances from each other. This study developed an autonomous system that monitors and admonishes physical distancing violators by spotting lights at them and playing a pre-recorded audio to remind them to keep their distance. It uses a downward-looking Fisheye IP camera to take a picture of the platform every 30 s. It uses Faster R-CNN with Detectron2 Library to detect people. Violators are then identified as individuals who have less than 1 m of computed Euclidean distance between other people. The environment being monitored was divided into four quadrants, each of which is lit with COB LEDs (controlled by an Arduino UNO) if they contain at least one violator. At the same time, a corresponding audio admonishment is being played through a loudspeaker. The performed single-sample t-test proves the accuracy of the Euclidean distance measurement of the system since the probability value of 0.6969, derived from the computed t-statistic of 0.3914, is significantly greater two-tailed significance level of 0.05.
Interdisciplinary skills for final qualificationassessment scenario: survey study for European end Oriental languages programs

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of education workflow. Foreign Languages Acquisition and Linguistic Educationassessment are fundamentally interdisciplinary processes, informed by the nature of linguistic content and types of communicative and professional activities. Cross-sectorial factors of societal change, that provide thebackdrop for an interdisciplinary skillset critical transformation, crucial forthe COVID-19 emergency educational framework, are considered. The studyis based on identification of various interdisciplinary competency principles,derivative of 21st century skills for university staff members and projecteddigital literacy requirements. It is determined how in the situation of theCpvid-19 pandemic lockdown all elements of the Final QualificationAssessment at Borys Grinchenko Kyiv University, Ukraine, for European andOriental Languages programs have been relegated to the digital, remote orhybrid format with the use of ICT tools and skills that comprise aninterdisciplinary realm of Foreign Languages acquisition and assessment.Every step of the scenario adaptation to digital format required accelerateddevelopment of interdisciplinary skills of all participants and officials andcross-sectorial activities, otherwise not carried out through assessment ofForeign Languages programs.
Multilingual Coreference Resolution with Harmonized Annotations

In this paper, we present coreference resolution experiments with a newly created multilingual corpus CorefUD (Nedoluzhko et al., 2021).We focus on the following languages: Czech, Russian, Polish, German, Spanish, and Catalan.In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models -for Slavic languages and for all the languages together.We rely on an end-to-end deep learning model that we slightly adapted for the CorefUD corpus.Our results show that we can profit from harmonized annotations, and using joined models helps significantly for the languages with smaller training data.
Μελέτη και δημιουργία υπηρεσίας κατανεμημένης επίθεσης Brute-Force

The purpose of this thesis is to provide an immersive experience in developing and using a system that distributes the workload that is required to complete a Brute Force attack. The application that was built, works either as a client or as a server in order to create through the participating devices a network that will be able to take over parts of the total workload. The necessary input data is entered using arguments at the start of the application.
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation

Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering (QA) and Fact Verification. Recently, pre-trained sequence to sequence (seq2seq) models have proven to be very effective in jointly making predictions, as well as generating NL explanations. However, these models have many shortcomings; they can fabricate explanations even for incorrect predictions, they are difficult to adapt to long input documents, and their training requires a large amount of labeled data. In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. FiD-Ex significantly improves over prior work in terms of explanation metrics and task accuracy on five tasks from the ERASER explainability benchmark in both fully supervised and few-shot settings.
The Influence of Explicit Instruction Learning Model toward 11th Grade Student’s Learning Outcomes

This research aims to determine the influence of explicit instriction learning model toward 11th grade student‟s learning outcomes in accounting subjects at SMA Negeri 16 Makassar. The Variables in this research are explicit instriction learning model (X) and learning outcomes (Y). The population is the entire students of XI IPS clas at SMA Negeri 16 Makassar school 2016/2017 that consist of 147 students, while the sample determination uses purposive sampling technique with the number of sample is 36 students, technique of data collection used are test and observation. Technique of data analysis was done by using analysis of data validity and statistic analysis of SPSS 22 for windows. Based on the results of data analysis simple linear regression Y= -0.162+0,861X, where in every addition of one value of lerning model „explicit instruction‟, the lerning result incrased to 2.247. The mean score of pretest was 52 and posttest was 88.083 from the correlation coefficient of 0.247. Based on the t-test analysis, it was found that the tcalculation was 5.515 which was bigger than ttable 2.000. The R2 (R Square value was 0.247. The t-test result showed significant increase after treatment.
Σύνθεση και ανίχνευση πλαστών (deep fake) βίντεο με αλγορίθμους βαθιάς μηχανικής μάθησης

Deep fake video generation uses machine learning and artificial intelligence. The synthesized videos can be used with good intentions, such as humorous videos. In some cases, however, their use can be malicious. That is when they aim to deceive through their promotion as supposedly real videos. Due to the potentially great influence that these videos can have on the public sphere, it is necessary to develop models that can identify such cases.In the context of this paper, the positive and negative ways of using deepfake video and image generation technologies are pointed out. The categories of fake videos are then identified and existing deepfake video generation algorithms are presented.After that, methods that have been developed for deepfake video detection are referenced. These techniques are categorized into methods that take into account temporal information, which is the change of features within a sequence of video frames, and into methods that rely solely on the spatial information extracted from each frame.Finally, the architecture of four machine learning models is presented. These are the R3D, MC3, R2Plus1D and I3D models. In this present dissertation, these models were trained in the Celeb-DF-v2 dataset, with the aim of classifying videos as fake or authentic. Their results are presented, evaluated and compared in terms of the ability to detect deepfake videos.
The Impact of Positional Encodings on Multilingual Compression

In order to preserve word-order information in a non-autoregressive setting, transformer architectures tend to include positional knowledge, by (for instance) adding positional encodings to token embeddings. Several modifications have been proposed over the sinusoidal positional encodings used in the original transformer architecture; these include, for instance, separating position encodings and token embeddings, or directly modifying attention weights based on the distance between word pairs. We first show that surprisingly, while these modifications tend to improve monolingual language models, none of them result in better multilingual language models. We then answer why that is: sinusoidal encodings were explicitly designed to facilitate compositionality by allowing linear projections over arbitrary time steps. Higher variances in multilingual training distributions requires higher compression, in which case, compositionality becomes indispensable. Learned absolute positional encodings (e.g., in mBERT) tend to approximate sinusoidal embeddings in multilingual settings, but more complex positional encoding architectures lack the inductive bias to effectively learn cross-lingual alignment. In other words, while sinusoidal positional encodings were designed for monolingual applications, they are particularly useful in multilingual language models.
A Corpus Study of Creating Rule-Based Enhanced Universal Dependencies for German

In this paper, we present a first attempt at enriching German Universal Dependencies (UD) treebanks with enhanced dependencies.Similarly to the converter for English (Schuster and Manning, 2016), we develop a rule-based system for deriving enhanced dependencies from the basic layer, covering three linguistic phenomena: relative clauses, coordination, and raising/control.For quality control, we manually correct or validate a set of 196 sentences, finding that around 90% of added relations are correct.Our data analysis reveals that difficulties arise mainly due to inconsistencies in the basic layer annotations.We show that the English system is in general applicable to German data, but that adapting to the particularities of the German treebanks and language increases precision and recall by up to 10%.Comparing the application of our converter on gold standard dependencies vs. automatic parses, we find that F1 drops by around 10% in the latter setting due to error propagation.Finally, an enhanced UD parser trained on a converted treebank performs poorly when evaluated against our annotations, indicating that more work remains to be done to create gold standard enhanced German treebanks.
Information Retrieval based on Cluster Analysis Approach

The huge volume of text documents available on the internet has made it difficult to find valuable information for specific users. In fact, the need for efficient applications to extract interested knowledge from textual documents is vitally important. This paper addresses the problem of responding to user queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a cluster-based information retrieval framework was proposed in this paper, in order to design and develop a system for analysing and extracting useful patterns from text documents. In this approach, a preprocessing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector Space Model (VSM) is performed to represent the dataset. The system was implemented through two main phases. In phase 1, the clustering analysis process is designed and implemented to group documents into several clusters, while in phase 2, an information retrieval process was implemented to rank clusters according to the user queries in order to retrieve the relevant documents from specific clusters deemed relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision (P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
The Effect of Bureaucratic School Structure on Teachers' Job Satisfaction: The Mediator Role of the Organizational Justice

The purpose of this study is to examine the mediating role of organizational justice in the effects of bureaucratic school structure on teachers' job satisfaction.For this, we reached 449 teachers working in the city centre of Mersin.Enabling School Structure, Organizational Justice, and Job Satisfaction Scales were used to collect the study data.Results revealed that teachers thought the schools they worked in had a moderate level of enabling bureaucratic structure and a low obstructive bureaucratic structure.A weak association was found between bureaucratic school structure dimensions and job satisfaction, while a moderate association was found between bureaucratic school structure dimensions and organizational justice.Besides, a weak association was found between job satisfaction and organizational justice.Structural equation modelling was conducted to examine the mediating role of organizational justice in the effect of bureaucratic school structure on job satisfaction.Results revealed that organizational justice acted as a mediator in the effects of enabling bureaucracy on job satisfaction.However, because the obstructive bureaucratic structure did not have any meaningful effect on organizational justice perception, it was not possible to mention its mediating effect.
Mitigating Language-Dependent Ethnic Bias in BERT

In this paper, we study ethnic bias and how it varies across languages by analyzing and mitigating ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. To observe and quantify ethnic bias, we develop a novel metric called Categorical Bias score. Then we propose two methods for mitigation; first using a multilingual model, and second using contextual word alignment of two monolingual models. We compare our proposed methods with monolingual BERT and show that these methods effectively alleviate the ethnic bias. Which of the two methods works better depends on the amount of NLP resources available for that language. We additionally experiment with Arabic and Greek to verify that our proposed methods work for a wider variety of languages.
Chapter 5. Scaling the Toolkit for Greater Reach

Chapter 5 of Library Technology Reports (vol. 57, no. 8), “Using the Toward Gigabit Libraries Toolkit,” explains the process and goals of the next iteration of the toolkit—Gigabit Libraries and Beyond—and discusses the project participants and the subject matter experts who are on the advisory board.
Learning from Mistakes – a Framework for Neural Architecture Search

Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In this paper, we investigate if this human learning strategy can be applied in machine learning. We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision. We formulate LFM as a three-stage optimization problem: 1) learner learns; 2) learner re-learns focusing on the mistakes, and; 3) learner validates its learning. We develop an efficient algorithm to solve the LFM problem. We apply the LFM framework to neural architecture search on CIFAR-10, CIFAR-100, and Imagenet. Experimental results strongly demonstrate the effectiveness of our model.
Management of Cultural Resources in the Riouwstraat Area (LLRE Martadinata) in Bandung from the Perspective of Cultural Resources Management

Riouwstraat area in Bandung from the perspective of cultural resources management (CRM) is an effort to manage cultural heritage areas in the midst of the widespread use of cultural heritage buildings for business purposes. The background of the transfer of cultural heritage functions occurs because of the transformation process that is supported by various factors, namely: economic which is also related to socio-cultural conditions, strategic location, and government policies. The Riouwstraat area management strategy is carried out by identifying and analyzing important values. The solution for managing this area is sought to accommodate the interests of investors while still upholding conservation; synergize the interests of stakeholders with policy makers; and prioritizing the principle of benefit to society.