Reviewerly/exHarmony huggingface dataset:
Classification of the Human Development Index in Indonesia Using the Bootstrap Aggregating Method

Successful development of the quality of human life in a region is determined by the Human Development Index (HDI). Human development performance based on the HDI can be measured: long and healthy life, knowledge, and a decent standard of living. The HDI is usually grouped into several categories to facilitate the classification of the HDI level of each region. This study aimed to determine the ability of the bootstrap aggregating (bagging) method to classify the HDI by district/city. Bagging is a stochastic machine learning approach that can eliminate the variance of the classifier by producing a bootstrap ensemble to obtain better accuracy results. The dependent variable in this study was the HDI by district/city in 2020. In contrast, life expectancy at birth, expected years of schooling, mean years of schooling, and real expenditure per capita are adjusted as independent variables. Bagging was applied to the high and low categories of HDI data. The bagging method demonstrated good classification performance due to only eight classification errors, namely the HDI data which should be in the high category but classified into the low category by the bagging method. Based on the results of calculations with 25 replications, it can be concluded that the bagging method has a very good performance, with an accuracy value of 92.3%, the sensitivity of 100%, and specificity of 83.33%. The bagging method is considered very good for the classifying the HDI by district/city in Indonesia in 2020 because it has a balanced accuracy of 91.67%.
LaoPLM: Pre-trained Language Models for Lao

Trained on the large corpus, pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. They can benefit multiple downstream natural language processing (NLP) tasks. Although PTMs have been widely used in most NLP applications, especially for high-resource languages such as English, it is under-represented in Lao NLP research. Previous work on Lao has been hampered by the lack of annotated datasets and the sparsity of language resources. In this work, we construct a text classification dataset to alleviate the resource-scare situation of the Lao language. We additionally present the first transformer-based PTMs for Lao with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base, and evaluate it over two downstream tasks: part-of-speech tagging and text classification. Experiments demonstrate the effectiveness of our Lao models. We will release our models and datasets to the community, hoping to facilitate the future development of Lao NLP applications.
Demonstration of the Rodeo Algorithm on a Quantum Computer

The rodeo algorithm is an efficient algorithm for eigenstate preparation and eigenvalue estimation for any observable on a quantum computer. The only requirement is that the initial state has sufficient overlap probability with the desired eigenstate. While it is exponentially faster than well-known algorithms such as phase estimation and adiabatic evolution for eigenstate preparation, it has yet to be implemented on an actual quantum device. In this work, we apply the rodeo algorithm to determine the energy levels of a random one-qubit Hamiltonian, resulting in a relative error of $0.08\%$ using mid-circuit measurements on the IBM Q device Casablanca. This surpasses the accuracy of directly-prepared eigenvector expectation values using the same quantum device. We take advantage of the high-accuracy energy determination and use the Hellmann-Feynman theorem to compute eigenvector expectation values for different random one-qubit observable. For the Hellmann-Feynman calculations, we find a relative error of $0.7\%$. We conclude by discussing possible future applications of the rodeo algorithm for multi-qubit Hamiltonians.
Implementation of Differential Sensing Scheme to Remove Panel Noise in Touchscreen Controller

Differential sensing scheme is an improvement method from single-line sensing to cancel display or panel noise in touchscreen system. However, differential sensing method raised a multiple-touches cancelation issue. As a consequence, the recovery algorithm is invented to overcome those issues. This paper introduces several methods to implement differential sensing scheme with recovery algorithm to remove panel noise and white noise in touchscreen system.
ViraPart: A Text Refinement Framework for ASR and NLP Tasks in Persian

The Persian language is an inflectional SOV language. This fact makes Persian a more uncertain language. However, using techniques such as ZWNJ recognition, punctuation restoration, and Persian Ezafe construction will lead us to a more understandable and precise language. In most of the works in Persian, these techniques are addressed individually. Despite that, we believe that for text refinement in Persian, all of these tasks are necessary. In this work, we proposed a ViraPart framework that uses embedded ParsBERT in its core for text clarifications. First, used the BERT variant for Persian following by a classifier layer for classification procedures. Next, we combined models outputs to output cleartext. In the end, the proposed model for ZWNJ recognition, punctuation restoration, and Persian Ezafe construction performs the averaged F1 macro scores of 96.90\%, 92.13\%, and 98.50\%, respectively. Experimental results show that our proposed approach is very effective in text refinement for the Persian language.
Garmr: Defending the gates of PKU-based sandboxing

Memory Protection Keys for Userspace (PKU) is a recent hardware feature that allows programs to assign virtual memory pages to protection domains, and to change domain access permissions using inexpensive, unprivileged instructions. Several in-process memory isolation approaches leverage this feature to prevent untrusted code from accessing sensitive program state and data. Typically, PKU-based isolation schemes need to be used in conjunction with mitigations such as CFI because untrusted code, when compromised, can otherwise bypass the PKU access permissions using unprivileged instructions or operating system APIs. Recently, researchers proposed fully self-contained PKUbased memory isolation schemes that do not rely on other mitigations. These systems use exploit-proof call gates to transfer control between trusted and untrusted code, as well as a sandbox that prevents tampering with the PKU infrastructure from untrusted code. In this paper, we show that these solutions are not complete. We first develop two proof-of-concept attacks against a state-of-the-art PKU-based memory isolation scheme. We then present Garmr, a PKU-based sandboxing framework that can overcome limitations of existing sandboxes. We apply Garmr to several memory isolation schemes and show that it is practical, efficient and secure.
ABOUT DEVELOPMENT OF SKILLS OF ELEMENTARY CLASSES TEACHERS TO PEDAGOGICALLY SUPPORT THE RESEARCH (PROJECT) ACTIVITY OF STUDENTS

"An analysis of the literature and the practice of forming a teacher’s own skills to organize and accompany research (project) activities indicates significant difficulties that he encounters in his daily activities. The insufficient level of scientific and theoretical knowledge, methodological and methodological training does not allow the teacher to successfully set and independently solve the problems of accompanying students in the educational process. Many teachers experience significant difficulties in organizing and supporting research activities: initiating and defining a research topic, formulating a scientific apparatus, preparing tools for collecting information and conducting an experiment. The article substantiates the conditions for the development of teacher’s skills to organize and carry out pedagogical support of research and project activities of primary school students. These conditions contribute to the formation of these skills of a primary school teacher, especially since the system of continuous teacher education provides significant opportunities for this. The described work carried out based on the ideas of cooperation pedagogy, stimulating productive interaction of community members, which contributes to the development of the qualities necessary for pedagogical support of students’ activities. "
Elections Cameroon (Elecam) and the Adoption of Information and Communication Tools (ICTs) to Improve Electoral Governance in the Electoral Process in Cameroon

Elections Cameroon (Elecam) and the Adoption of Information and Communication
Fuzzy Genetic Grey Wolf based Deep Learning Model For Classification on Breast Cancer Dataset

Neural Network is based on biological evolution of human brain. It is an emerging approach used to develop new and robust competing techniques. For solving learning and data analysis problems neural network techniques are used for better performance. In Medical data analysis, neural network, genetic and fuzzy techniques are widely used. In this paper, we proposed a new fuzzy genetic grey wolf based deep learning model on breast cancer dataset. Deep Convolution Neural Network (CNN) is an evolutionary algorithm, which mimics the process of human brain.CNN algorithm frequently used to solve classification problems. In medical domain, we need more accuracy when compared to other domains, because it closely related with the human life. Breast Cancer is one of the dangerous diseases among women in India as well as in the whole world.Deep learning based techniques has developed rapidly in recent years, mostly in classification. The proposed fuzzy genetic grey wolf based deep learning model produces better accuracy for classification on breast cancer dataset.
Efektifitas Penggunaan Modul Pembelajaran Terhadap Peningkatan Pengetahuan Siswa Tentang Influensa Like Illness

Children are a group with a high level of vulnerability especially because they are in the process of growth. Disrupted growth due to disease will affect the degree of health. Growing children are at risk for decreased immune system resulting in children getting sick, especially respiratory diseases, namely Influenza Like Illness which most often affects preschool or school age children. Data from the West Seram Health Office The 10 most common diseases in 2019 (January-July) were occupied by influenza like illnesss with a total of 2,552 cases (male = 1,228 cases; 1,324 women. There are 1514 children (0-14 years old). The use of modules is one method that can develop positive knowledge and attitudes to health. The purpose of this study is to determine the effectiveness of the use of learning modules to increase students' knowledge about Influenza Like Illness. Method: This quantitative research uses a learning module with a quasi - experimental design with pre and post test One Group design. The population is all students of the Patinia Public Elementary School, Patinia Hamlet, Kawah Village, West Seram District, which is the working area of Puskesmas piru, West Seram Regency with a purposive sampling technique. as many as 20 students from perwa kilan each of class IV, V and class VI, so that the number of research subjects is 60 students. Processing data with the N-Gain Score experimental class with paired sample t test. Result: shows that the mean value (mean) of N-Gain percent is 57.8553. Based on the category table of the interpretation of the effectiveness of the N-Gain value (%) above, it can be concluded that the use of learning modules is effective enough to increase students' knowledge about Influenza Like Illness. Conclusions: The use of modules in learning about Influenza Like Illness is very effective in increasing the knowledge of elementary school students Keywords: i nfluensa l ike i llness ; m odule
Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation

This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. Following this idea, we present SixT+, a strong many-to-English NMT model that supports 100 source languages but is trained with a parallel dataset in only six source languages. SixT+ initializes the decoder embedding and the full encoder with XLM-R large and then trains the encoder and decoder layers with a simple two-stage training strategy. SixT+ achieves impressive performance on many-to-English translation. It significantly outperforms CRISS and m2m-100, two strong multilingual NMT systems, with an average gain of 7.2 and 5.0 BLEU respectively. Additionally, SixT+ offers a set of model parameters that can be further fine-tuned to other unsupervised tasks. We demonstrate that adding SixT+ initialization outperforms state-of-the-art explicitly designed unsupervised NMT models on Si<->En and Ne<->En by over 1.2 average BLEU. When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12.3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder.
Apache Nemo: A Framework for Optimizing Distributed Data Processing

Optimizing scheduling and communication of distributed data processing for resource and data characteristics is crucial for achieving high performance. Existing approaches to such optimizations largely fall into two categories. First, distributed runtimes provide low-level policy interfaces to apply the optimizations, but do not ensure the maintenance of correct application semantics and thus often require significant effort to use. Second, policy interfaces that extend a high-level application programming model ensure correctness, but do not provide sufficient fine control. We describe Apache Nemo, an optimization framework for distributed dataflow processing that provides fine control for high performance and also ensures correctness for ease of use. We combine several techniques to achieve this, including an intermediate representation of dataflow, compiler optimization passes, and runtime extensions. Our evaluation results show that Nemo enables composable and reusable optimizations that bring performance improvements on par with existing specialized runtimes tailored for a specific deployment scenario. Apache Nemo is open-sourced at https://nemo.apache.org as an Apache incubator project.
Parallelisable Existential Rules: a Story of Pieces

In this paper, we consider existential rules, an expressive formalism well adapted to the representation of ontological knowledge, as well as data-to-ontology mappings in the context of ontology-based data integration. The chase is a fundamental tool to do reasoning with existential rules as it computes all the facts entailed by the rules from a database instance. We introduce parallelisable sets of existential rules, for which the chase can be computed in a single breadth-first step from any instance. The question we investigate is the characterization of such rule sets. We show that parallelisable rule sets are exactly those rule sets both bounded for the chase and belonging to a novel class of rules, called pieceful. The pieceful class includes in particular frontier-guarded existential rules and (plain) datalog. We also give another characterization of parallelisable rule sets in terms of rule composition based on rewriting.
SemSketches­2021: experimenting with the machine processing of the pilot semantic sketches corpus

The paper deals with elaborating different approaches to the machine processing of semantic sketches.It presents the pilot open corpus of semantic sketches.Different aspects of creating the sketches are discussed, as well as the tasks that the sketches can help to solve.Special attention is paid to the creation of the machine pro cessing tools for the corpus.For this purpose, the SemSketches2021 Shared Task was organized.The participants were given the anonymous sketches and a set of contexts containing the necessary predicates.During the Task, one had to assign the proper contexts to the corresponding sketches.
INFORMATION QUALITY AND THE ORGANIZATIONAL EFFECTIVENESS: THE MODERATING EFFECTS OF ORGANIZATIONAL CULTURE AMONG CONVENTIONAL AND ISLAMIC BANKS

Quality of information is a priceless asset for organization to possess as its assist in carrying out business plans and changes. These business changes usually support the management executive in decision makings. In view of that, this study examines the information quality in AIS and its effects on organizational performance among conventional and Islamic banks in Jordan. To achieve that, proportionate stratified random sampling is applied to the information system users of sixteen conventional and Islamic banks in Jordan. Total copies of 600 questionnaires were distributed and only 250 among the returned copies were valid, suggesting a valid response rate of 41.7%. The study adopts the partial least square (Smart PLS 3) method to enhance the data analysis and perform hypotheses testing. Findings clearly show that quality of information is the key for business growth as it indicates a positive effect on organizational performance. Further result shows that organizational culture improves and increases business performance when combined with information quality. For this reason, conventional and Islamic banks in Jordan should have well-developed AIS as it assists organizations to -attain higher performance. There is need for more development in management skills to fully exploit the AIS in order to realize a greater organizational performance. In other words, full implementation of AIS should be given more priority by the managements of these conventional and Islamic banks.
Research Guides: Virtual Reality at PUL: Services

Virtual reality (VR) is a realistic and immersive simulation of a virtual environment, experienced through interactive software and hardware. VR technology is available to the Princeton University community at the Stokes Library in Wallace Hall.
Providing Algorithm for Embedding Virtual Machines on Data Centers Based on Clustering and Honey Bee Algorithm

Today, it is one of the important discussions is the resource management component and placement technique in cloud data centers. Optimal placement prevents the emergence of a situation where some computing resources are heavy, while other resources are idle as well as wasteful energy consumption. Timing techniques are usually aimed at reducing the response time of the program. If, in addition to taking time, energy is not controlled, it affects the costs and quality of service. The method presented in this paper is based on the consideration of both time and energy factors on effective resource management. The proposed method is the use of clustering on requests and allocation of all sets by defining the function for the optimized bee algorithm on computational resources. The experimental results indicate the impact of 6.3% of energy consumption and 5.0% of the time response that were clustered and deployed using optimized bee algorithm on the best source.
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems

As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm. In this paper, we investigate the interplay between vulnerabilities of the image scaling procedure and machine learning models in the decision-based black-box setting. We propose a novel sampling strategy to make a black-box attack exploit vulnerabilities in scaling algorithms, scaling defenses, and the final machine learning model in an end-to-end manner. Based on this scaling-aware attack, we reveal that most existing scaling defenses are ineffective under threat from downstream models. Moreover, we empirically observe that standard black-box attacks can significantly improve their performance by exploiting the vulnerable scaling procedure. We further demonstrate this problem on a commercial Image Analysis API with decision-based black-box attacks.
THE EFFECT OF USE OF POWER POINT LEARNING MEDIA ON STUDENT LEARNING MOTIVATION AT SMK BINA ISLAM MANDIRI KERSANA

The purpose of this study was to determine the effect of power point learning media on student motivation at SMK Bina Islam Mandiri Kersana. The method of data collection in this study was to distribute questionnaires to 19 PPL students. The data analysis technique in this study used simple linear regression SPSS 20. From the results of the analysis of the coefficient of determination obtained a value of 21.5% which means that the use of powerpoint media has a contribution to student learning motivation of 21.5% while the t test value is obtained at 0.045 which means the use of power point learning media has an effect on students' learning motivation.
EMERGENCY REMOTE FOREIGN LANGUAGE CLASS MANAGEMENT AS A TERTIARY TEACHING ISSUE

The article discusses some features of teaching a foreign lan- guage in the context of an emergency remote shift, that is teaching unplanned and urgently switched from offline to online. It is likely that this format will keep a foothold in future to some extent, so it is worth paying attention to the development of teaching methods for such new specific contexts. The author examines such teaching from the perspectives of the general theory of management and the psychology of learning, incorporating the computer- mediated one. The purpose of the article is to propose an approach to op- timizing “classroom work” in an emergency remote format. The problem of management in a remote foreign language session is considered as a problem of teacher’s understanding of the entire focuses of pedagogical attention and selective intervention on those that need to be adjusted at the moment. Based on the generalization of the author’s own observations, the experience of teaching a foreign language under remote conditions, as well as the analysis of modern scientific research literature and a questionnaire survey of students, a conclusion was made about the productivity of using the basic concepts and provisions of the management theory in relation to the novel nonconventional conditions of language teaching. The role of the educator is similar to that of a manager developing successful pedagogical strategies, some of which are recommended in this paper.