Reviewerly/exHarmony huggingface dataset:
APLIKASI SISTEM PAKAR UNTUK MENENTUKAN KEPRIBADIAN SISWA KELAS XII DENGAN METODE FORWARD CHAINING BERBASIS WEB (STUDI KASUS : SMA SANTO YOSEPH)

Technology hold important role on preparing resource for human, therefore maintaining goodtechnology by quality and quantity is a must. It can be seen by how fast in this technology era. With a greatand modern development of technology, there is a technology that can adopt how human think calledartificial intelligence that combine knowledge and data seeking for answer of trouble that human needResearcher write what is the meaning of system, information, and system information which is thebasis of expert system, researcher also write information about artificial intelligence and role of expertsystem. Researcher use forward chaining for counting. Researcher also write about web, personality, jungpsychology, and MBTI with 16 personality of MBTI.Researcher gather data by doing field observation, such as direct observation and interview withteacher from Santo Yoseph high school, and literature review such reading book, journals, and website forreference.This application made for determine personality of students Santo Yoseph high school based onMBTI and easier for teacher of Santo Yoseph high school for gathering data counseling guidance.Through step by step research and program development, researcher make a conclusion thatapplication that researcher made can determine personality based on MBTI dan easier for teacher of SantoYoseph high school for gathering data counseling guidance.Key words : Expert system, Forward chaining, MBTI (Myers-Brigss Type Indicator), Web
환경분야 시험 · 검사기관 현장평가 결과 분석

The on-site assessment of environmental testing and inspection laboratories is conducted every three years and consists of an evaluation of the operations and technology sectors and an evaluation of eight environmental media. This study analyzed the results of on-site assessments conducted on the environmental testing and inspection laboratories in 2017. The evaluations of the operations and technology sectors showed that it is necessary to supplement the independence of the internal quality control work, the appropriateness of the ability assessment when changing the personnel power and evaluating of equipment, and the verification of testing methods prompted by changes in facilities and equipments. Finally, four were the common points that emerged from the eight assessment areas, namely records management for sampling and experiment, management of laboratory waste, records management for consumables and standard reference materials.
The Abandonment of the Assignment of Subject Headings and Classification Codes in University Libraries due to the Massive Emergence of Electronic Books // El abandono de la asignación de encabezamientos de materia y códigos de clasificación en las bibliotecas universitarias debido a la incorporación masiva del libro electrónico.

The massive and unstoppable emergence of electronic books in libraries has altered their organization. This disruptive technology has led to structural changes. Currently, an e-book exists only if its metadata exists. The objective of this article is to analyse the impact that the massive incorporation of electronic books in university library systems is having in the processes of assignment of subject headings and classification codes. We carried out a survey of more than six hundred libraries, which means almost all the university libraries in Portugal, Spain, England, United States, Brazil, Sweden, Norway, Finland and Australia. From the results obtained, it is deduced that: 1) librarians expect ebooks to be provided with descriptive metadata related to the subject headings and classification codes; 2) the bibliographic records provided by publishers/providers seem to be improvable; 3) the quality of the metadata provided by the providers does not seem to be taken into account when selecting publishers for the purchase; 4) the discovery tools are also clearly improvable; 5) it seems that there is no frustration or stress among librarians about the changes produced in relation to technical processes; and, 6) it does not seem that we are facing a paradigm shift motivated by these issues.
Nativos digitales como eje dinamizador de procesos cognoscitivos contextualizados en instituciones educativas públicas venezolanas

The manuscript addresses the transformation in human behavior patterns, on the occasion of the impact, that information and communication technologies have exerted on contemporary society. It is a documentary article, with bibliographic design, where the bibliographic method was applied and the documentation would be used as a technique for the analysis and interpretation of the archived files. With the discussion of the findings achieved, he was able to reflect on the way in which the X generation in contrast to the Y generation, take advantage of the various technological resources in the educational processes. To conclude, it is stated that digital natives, have developed their own behavioral archetypes, which are adopted by other millennials, as gregarious subjects in a shared reality.
Coordinated Multi-Agent Exploration Using Shared Goals

Exploration is critical for good results of deep reinforcement learning algorithms and has drawn much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. It was recognized recently that noise-based exploration is suboptimal in multi-agent settings, and exploration methods that consider agents' cooperation have been developed. However, existing methods suffer from a common challenge: agents struggle to identify states that are worth exploring, and don't coordinate their exploration efforts toward those states. To address this shortcoming, in this paper, we proposed coordinated multi-agent exploration (CMAE): agents share a common goal while exploring. The goal is selected by a normalized entropy-based technique from multiple projected state spaces. Then, agents are trained to reach the goal in a coordinated manner. We demonstrated that our approach needs only 1%−5% of the environment steps to achieve similar or better returns than state-of-the-art baselines on various sparse-reward tasks, including a sparse-reward version of the Starcraft multi-agent challenge (SMAC).
Procedural generation of game bits and its effect on game user experience

Procedural Content Generation means the algorithmic creation of game content with limited or indirect user input. This technique is currently widespread in the game industry. However, its effects w ...
Learning with multimedia resources in subject Learning about the environment

he following work attempts to determine how primary school students use multimedia and visualisations in learning materials in science subjects. For the diploma thesis, we conducted a study involving second graders from a primary school in the region Central Slovenia. 42 students (22 boys and 20 girls) were included in the study by means of an eye tracker and a written test and were randomly divided into two experimental groups. We focused on whether the students were able to learn independently with the help of multimedia materials and how they were successful in doing so. The first group of students received learning material from a science textbook on learning about the environment. For the second group of students we enhanced the materials with content explanations and added enhanced learning materials. We introduced these changes to make it easier for the second group of students to understand the content of learning materials.The results indicate that the changes we made to the materials were beneficial.We found that both groups were equally successful in solving tasks that included reading maps and legends. We concluded that students would be able to master cartographic skills faster if they were given a map of an already familiar environment at the beginning of the study process. Teachers need to be aware of the student's cognitive level and adapt the tasks accordingly.By adding appropriate visual elements to the teaching materials (in our case maps, the experiment with a plastic bottle and graphic representations) we can improve their' understanding of the topic. We found that the students in the second group were able to present their answers better than the students in the first group because of the enhanced learning material. Heatmaps also showed us that the students of the second group were able to concentrate better on the essential elements of the learning material.Working with concrete materials continues to play a key role in teaching. The learning material must be developed step by step, starting with the concrete and moving on tomore abstract. Our research shows that the teachers have an important role in guiding the students. However, the teacher should use multimedia materials to support the learning process. Multimedia materials are effective as long as they do not contain too much irrelevant information. For further research, it would be useful to conduct an experiment in the classroom, in which one group of students is learning the subject matter through multimedia material and the other group is explained the same subject matter by the teacher.
INNOVATION EFFECT THROUGH RESEARCH AND DEVELOPMENT ON ECONOMIC GROWTH IN 35 OECD COUNTRIES

This paper analyzes the effect of research and development (R&amp;#38;D) expenditures on economic growth in the Organization of Economic Cooperation and Development (OECD) countries over the period 2000-2016. This study conducts an empirical analysis using a multiple regression model. The main findings confirm that an increase in research and development expenditure by 1% would generate an increase of real GDP growth rate to 2.83 %. The implication emerging from this study is that government and institutions need to increase investment in R&amp;#38;D expenditures to fulfill inclusive economic growth perspective.
Paraphrase Identification Based on ResNeXt

The misuse of paraphrase generation technology has flooded the Internet with low-quality, unclear manuscripts, deteriorating the user's reading experience. This paper mainly investigates into the paraphrase identification from the deep learning perspective, and proposes a paraphrase identification model based on deep ResNeXt. Our model captures the semantic and syntactic features of the given text with the help of deep neural network. Specifically, we select multiple convolution kernels with different sizes to extract the features of word sequences of different lengths. At the same time, the residual block which helps build a paraphrase identification model suitable for various current platforms. The data preprocessing includes data cleaning, word segmentation, and word vectorization. Experiments on benchmark dataset MSRPC show that our model achieved a strong result of 85.4% on accuracy rate.
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation.

Mixed-Integer Quadratic Programming (MIQP) has been identified as a suitable approach for finding an optimal solution to the behavior planning problem with low runtimes. Logical constraints and continuous equations are optimized alongside. However, it has only been formulated for a straight road, omitting common situations such as taking turns at intersections. This has prevented the model from being used in reality so far. Based on a triple integrator model formulation, we compute the orientation of the vehicle and model it in a disjunctive manner. That allows us to formulate linear constraints to account for the non-holonomy and collision avoidance. These constraints are approximations, for which we introduce the theory. We show the applicability in two benchmark scenarios and prove the feasibility by solving the same models using nonlinear optimization. This new model will allow researchers to leverage the benefits of MIQP, such as logical constraints, or global optimality.
Wasserstein Distributional Normalization : Nonparametric Stochastic Modeling for Handling Noisy Labels

We propose a novel Wasserstein distributional normalization (WDN) algorithm to handle noisy labels for accurate classification. In this paper, we split our data into uncertain and certain samples based on small loss criteria. We investigate the geometric relationship between these two different types of samples and enhance this relation to exploit useful information, even from uncertain samples. To this end, we impose geometric constraints on the uncertain samples by normalizing them into the Wasserstein ball centered on certain samples. Experimental results demonstrate that our WDN outperforms other state-of-the-art methods on the Clothing1M and CIFAR-10/100 datasets, which have diverse noisy labels. The proposed WDN is highly compatible with existing classification methods, meaning it can be easily plugged into various methods to improve their accuracy significantly.
INCREASING STUDENTS’ ABILITY IN WRITING PROCEDURAL TEXT BY USING MAKE-A-MATCH METHOD

The purpose of this research was to find out how the make-a-match method improves the students ’ ability in mastering writing procedural text and also to investigate the class atmosphere when the make-a-match method was used in teaching writing procedural text. The approach used in this research was a qualitative approach and the design was collaborative classroom action research. This research was conducted at MAS Ulumuddin Lhokseumawe. The subjects of the research were the second-grade students which consist of 25 students. There were two cycles in this research. Each cycle was conducted in three meetings. The result of the research showed that the students score increased from the cycle I to cycle II. The pre-test of the cycle I score was 57.02. It meant that they did not meet the success indicators. After the treatment was given, the post-test of the cycle I score was 65.72. It meant there were improvements from the first test but it still did not meet the criteria of success and should continue to the cycle II. Then the post-test of the cycle II score was 76.02.  It suggests that there were some improvements from the second test and the students met the success indicators. Besides, make-a-match method could make the class atmosphere becoming more active especially when they matching the card to find its answer. Besides, make-a-match method gave enthusiastic situation and students became more active in discussing the meaning of the sentence contained in the cards.
Deep Learning Driven Image Segmentation in Medical Science - An Intense Learning

Segmentation of medical images is a required mechanism for assessment, preparation, treatment, etc. to achieve and localize malignancy or clear structural anomalies in medical images. For a body part or lesion area, a wide variety of automated medical image segmentation techniques, such as vessel and skin cancer, have been proposed. Deep learning can learn motive in computer vision to anticipate groups of objects that form an image for robust segmentation. The primary in-depth learning architecture used in image processing is a Convolutionary Neural Network (CNN) or special CNN systems such as AlexNet, VGG, Inception and ResNet. Deep computer vision models are typically taught and conducted to shorten the time required to handle graphics units (GPUs).A huge effort is made in this paper to discuss various image segmentation approaches and explains how images are segmented by deep learning power.Keywords-Artificial intelligence, Clinical practice, Healthcare, Machine learning, Medical science.
ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.
Stimulating Organizational Citizenship Behavior by Applying Organizational Commitment and Satisfaction

Purpose of the study: This study aims to apply organizational commitment and job satisfaction as an indicator to stimulate organizational citizenship behaviours. Methodology: The data collected by a questionnaire survey conducted among a group of hotel employees in Baghdad, Iraq. The 200 questionnaires distributed, the researchers received 149,were valid responses. The data were analyzed by AMOS software after ensuring validity, Reliability, and model fit.Main Findings:The results revealed that organizational commitment and job satisfaction play a positive and influential role in changing the organizational citizenship behaviours among hotel employees. Also, organizational commitment has a stronger impact on improving the behaviour of employees than job satisfaction. Research limitations/implications: The study contributes to human resource managers as it reveals the influential role affirms commitment and job satisfaction on altering staff behaviours towards organizational citizenship. Therefore, it is understood that more variables need to be considered,such as psychological contract and employees' empowerment.Novelty/Originality of this study: The finding can fill the gap in the literature related to this article in the Iraqi setting, where studies about organizational citizenship is not existing in this context.
More Communication Lower Bounds for Information-Theoretic MPC

We prove two classes of lower bounds on the communication complexity of information-theoretically secure multiparty computation. The first lower bound applies to perfect passive secure multiparty computation in the standard model with n = 2t+1 parties of which t are corrupted. We show a lower bound that applies to secure evaluation of any function, assuming that each party can choose to learn or not learn the output. Specifically, we show that there is a function H^* such that for any protocol that evaluates y_i = b_i ⋅ f(x₁,...,x_n) with perfect passive security (where b_i is a private boolean input), the total communication must be at least 1/2 ∑_{i = 1}ⁿ H_f^*(x_i) bits of information.The second lower bound applies to the perfect maliciously secure setting with n = 3t+1 parties. We show that for any n and all large enough S, there exists a reactive functionality F_S taking an S-bit string as input (and with short output) such that any protocol implementing F_S with perfect malicious security must communicate Ω(nS) bits. Since the functionalities we study can be implemented with linear size circuits, the result can equivalently be stated as follows: for any n and all large enough g ∈ ℕ there exists a reactive functionality F_C doing computation specified by a Boolean circuit C with g gates, where any perfectly secure protocol implementing F_C must communicate Ω(n g) bits. The results easily extends to constructing similar functionalities defined over any fixed finite field. Using known techniques, we also show an upper bound that matches the lower bound up to a constant factor (existing upper bounds are a factor lg n off for Boolean circuits).Both results also extend to the case where the threshold t is suboptimal. Namely if n = kt+s the bound is weakened by a factor O(s), which corresponds to known optimizations via packed secret-sharing.
LibGuides: New Non-Fiction Shelves: 400s - Language

Includes - General Knowledge, Encyclopedias, Associations & Museums, Journalism, Quotations, Library Science and Rare Books
Einstein VI: General and Integrated Stein Variational Inference in NumPyro

Stein Variational Inference is a technique for approximate Bayesian inferencethat is recently gaining popularity since it combines the scalability of traditionalVariational Inference (VI) with the flexibility of non-parametric particle basedinference methods. While there has been considerable progress in developmentof algorithms, integration in existing probabilistic programming languages (PPLs)with an easy-to-use interface is currently lacking. EinStein VI is a lightweightcomposable library that integrates the latest Stein Variational Inference methodswith the NumPyro PPL. Inference with EinStein VI relies on ELBO-within-Stein tosupport use of custom inference programs (guides), non-linear scaling of repulsionforce, second-order gradient updates using matrix-valued kernels and parametertransforms. We demonstrate the achieved synergy of the different Stein techniquesand the versatility of EinStein VI library by applying it on examples. Comparedto traditional Stochastic VI, EinStein VI is better at capturing uncertainty andrepresenting richer posteriors. We use several applications to show how one canuse Neural Transforms (NeuTra) and second-order optimization to provide betterinference using EinStein VI. We show how EinStein VI can be used to infer theparameters of a Latent Dirichlet Allocation model with a neural guide. The resultsindicate that Einstein VI can be combined with NumPyro’s support for automaticmarginalization to do inference over models with discrete latent variables. Finally,we introduce an example with a novel extension to Deep Markov Models, calledthe Stein Mixture Deep Markov Model (SM-DMM), which shows that EinStein VIcan be scaled to reasonably large models with over 500.000 parameters
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP

Reducing the heavy computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. In this context, recent works propose channel pruning via greedy channel selection to achieve practical acceleration and memory footprint reduction. We first show this channel-wise approach ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safely remove inactive weights during the pruning procedure. Furthermore, we show that these pruning methods cannot guarantee the given resource constraints are satisfied and cause discrepancy with the true objective. To this end, we formulate a principled optimization framework with discrete variable QCQP, which provably prevents any inactive weights and enables the exact guarantee of meeting the resource constraints in terms of FLOPs and memory. Also, we extend the pruning granularity beyond channels and jointly prune individual 2D convolution filters spatially for greater efficiency. Our experiments show competitive pruning results under the target resource constraints on CIFAR-10 and ImageNet datasets on various network architectures.