Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Berbasis Android Untuk Mata Pelajaran Dasar Desain Grafis Kelas X SMK Muhammadiyah 2 Klaten Utara

Basic Graphic Design Subjects is one of the C2 productive subjects in theMultimedia department at SMK Muhamadiyah 2 Klaten Utara which contains anintroduction to the basics of multimedia. Basic graphic design is a form of visualcommunication that uses images to convey information or messages as effectively aspossible. In graphic design, text is also considered an image because it is the result ofabstraction of symbols that can be sounded. Graphic design is applied incommunication design and fine art. In learning it is expected that students can mastergraphic design, both in terms of drawings and techniques. In addition students shouldalso be able to create a graphic design work or product with various models andshapes, for example graphic design products that are on the free market, such asbusiness cards, leaflets, CD covers, cassette covers, t-shirt designs, cover books ofsubjects, posters and others. However, in the learning process, the teacher still usesthe lecture method and assignments to students. Even though everyday schoolsstudents can carry a smartphone. Along with the development of technology,especially android-based mobile device technology that can be used as a support forlearning, one of them is by using android-based learning media in the Basic GraphicDesign subjects. This media is expected to be useful as a means of learning at schoolto make the smartphone function brought by students to school to support thelearning process. This research uses the Research and Development (R&D) method.The research model used is a ADDIE development model. This development modelconsists of five stages, namely analysis, design, development, implementation, andevaluation. This study aims to develop an Android-based learning media so that itcan maximize the smartphone functions carried by students. As for the results of theassessment of media experts by 74.5% with a decent assessment criteria, materialexperts by 81% with a very decent assessment criteria. While the assessment of 23Multimedia X grade students of SMK Muhammadiyah 2 Klaten Utara with anaverage percentage of 79% with a decent assessment category. It can be concludedthat the Android-based learning media on the Basic Subjects of Graphic Design arein the feasible category and can support the learning activities of students.Suggestions for further development of learning media will be more interesting ifthere is an animated display and sound effects.
ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts.

fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. State-of-the-art encoding models use a single global model (linear or non-linear) to predict brain activation given the stimulus. However, the critical assumption in these methods is that a priori different brain regions respond the same way to all the stimuli, that is, there is no modularity or specialization assumed for any region. This goes against the modularity theory, supported by many cognitive neuroscience investigations suggesting that there are functionally specialized regions in the brain. In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model. The key idea here is that each linear expert captures the behaviour of similar brain regions. Given a new stimulus, the utility of the proposed model is twofold (i) predicts the brain activation as a weighted linear combination of the activations of multiple linear experts and (ii) to learn multiple experts corresponding to different brain regions. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. This study helps in understanding the brain regions that are activated together given different kinds of stimuli. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration. Experiments on fMRI data from paradigm 1 [1]where participants view linguistic stimuli show that the proposed MoRE model has better prediction accuracy compared to that of conventional models.
Research Guides: Integrating Library Resources into Canvas: Home

This guide explains how faculty and instructors can more fully integrate library resources, such as journal articles, library research guides and ebooks, into a Canvas course site.
Service-Oriented Architecture In Automation Industries

This paper addresses issues and difficulties in the advancement of cutting edge installed gadgets, applications and its administration that Service-Oriented Architecture (SOA) handles in Automation Industries (AI).Because of SOAs expanding knowledge and traces of the methodology received, just as the business points of interest advancement, the assembling business with SOA expresses deep complexity and yet simple usage in process of automation. The future assembling undertakings will be portrayed by as often as possible changing business sector requests, time-to-showcase weight, constantly developing new advances and, most importantly, worldwide challenge.&#x0D; SOA and web innovation have evolved its route to support an unavoidably arranged world in which billions of individuals and trillions of devices and gadgets will be interconnected in different ways. As a component of this development, Internet innovation is rising as the essential bearer for interconnecting – utilized in mechanical mechanization, car hardware, building controls, home robotization, service automation, and so on.
Single Camera Training for Person Re-identification

Person re-identification (ReID) aims at finding the same person in different cameras. Training such systems usually requires a large amount of cross-camera pedestrians to be annotated from surveillance videos, which is labor-consuming especially when the number of cameras is large. Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera. To the best of our knowledge, this setting was never studied before. SCT enjoys the advantage of low-cost data collection and annotation, and thus eases ReID systems to be trained in a brand new environment. However, it raises major challenges due to the lack of cross-camera person occurrences, which conventional approaches heavily rely on to extract discriminative features. The key to dealing with the challenges in the SCT setting lies in designing an effective mechanism to complement cross-camera annotation. We start with a regular deep network for feature extraction, upon which we propose a novel loss function named multi-camera negative loss (MCNL). This is a metric learning loss motivated by probability, suggesting that in a multi-camera system, one image is more likely to be closer to the most similar negative sample in other cameras than to the most similar negative sample in the same camera. In experiments, MCNL significantly boosts ReID accuracy in the SCT setting, which paves the way of fast deployment of ReID systems with good performance on new target scenes.
Regression Planning Networks

Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accuracy of the prediction model. On the other hand, classical symbolic planners show remarkable capabilities in solving long-horizon tasks, but they require predefined symbolic rules and symbolic states, restricting their real-world applicability. In this work, we combine the benefits of these two paradigms and propose a learning-to-plan method that can directly generate a long-term symbolic plan conditioned on high-dimensional observations. We borrow the idea of regression (backward) planning from classical planning literature and introduce Regression Planning Networks (RPN), a neural network architecture that plans backward starting at a task goal and generates a sequence of intermediate goals that reaches the current observation. We show that our model not only inherits many favorable traits from symbolic planning, e.g., the ability to solve previously unseen tasks but also can learn from visual inputs in an end-to-end manner. We evaluate the capabilities of RPN in a grid world environment and a simulated 3D kitchen environment featuring complex visual scenes and long task horizons, and show that it achieves near-optimal performance in completely new task instances.
Eksperimen Model Pembelajaran POE (Predict Observe Explain) dan STEM (Science Technology Engineering Mathematics) terhadap Hasil Belajar Matematika Ditinjau dari Intelegensi Siswa Kelas VIII

The purpose of this research is to analyze: (1) the influence of the STEM learning model and POE on mathematics learning outcomes. (2) the influence of students' intelligence quotient level on mathematics learning outcomes. (3) there is an interaction between the learning model with the intelligence quotient of students on the learning outcomes of mathematics. This type of research design experimental research with quantitative relation. The population in this research as much as students of class VIII SMP Muhammadiyah 1 Surakarta. Samples taken in this research as much as 3 classes, the class of the first experiments with a model of learning STEM, the second experiment with the model class learning POE, and classroom learning model with conventional controls. Sampling techniques using cluster random sampling. Data collection techniques with methods of tests and documentation. Analytical techniques used in this research is the analysis of variansi two lines with the same cells. Based on the results of the research with a 5% significance level, obtained: (1) there is the influence of the STEM and POE learning models on mathematics learning outcomes, the STEM and POE classes are better when compared to Conventional classes. (2) there is influence of intelligence quotient on learning outcomes of mathematics, clever intelligence quotient is better than above average and average level. (3) there is no interaction between the learning model and the intelligence quotient on student learning outcomes.
Presentation, The Best Model for Teaching Speaking for Adults Students

The aim of the research is to show that presentation is one of the best models for teaching speaking for Adult students. This research and development study consists of exploration, designing model draft, validating model, experts jugment, revising model, implementing model, data description, data analysis, findings and discussion, recommendation, revising, the final model and dissemination. Technique of data collection was obtained by using test, interview, and observation; Technique of data analyze for quantitative data used non independent t-test. The researcher also used descriptive statistic including highest, average, and lowest scores. To analyze the qualitative data, the researcher used trianggulation and flow model of analysis. The research results showed that The first model results showed that: it improved the students’ bravery to speak up in front of the audience and made a better condition of the students’ speaking environment. The second model results showed that the development of the students’ speaking ability growing up rapidly and their grammar and vocabulary also getting better. The Final Model results showed that there is very clear improvement on their bravery to speak up infront of the audience. Their learning progress and speaking ability grew up rapidly, theirgrammar and vocabulary also increased significantly. The computation result showed thatall of the results is higher than tt (2,02). It showed that Developing a model of teachingspeaking through presentationis good, suitable and accurate for teaching and improvingthe Students’ Speaking Ability of accounting education of UMS is proved correct andsignificant.
Monitoring of Fake Reviews using Support Vector Machine and IP Tracking

In the modern era of digitization, online shopping has become an emerging trend. The buyers usually check the supposition of others before buying a product . The reviews given to a solitary item decides if the item holds credibility.Maker can pick up knowledge into its items quality and shortcomings dependent on the reviews of the clients. In this way, client reviews play an important role in ecommerce, since numerous choices are made dependent on them. Nowadays it is seen that the organizations will in general give good reviews to its very own item all together to build its sales ,and on other hand there are a few customers who intend to either fame of defame a product by giving fake reviews. Hence ,a framework which can recognize such kinds of fraud reviws, be it positive or negative is required .The proposed framework will use the support vector machine and ip tracking techniques for achieving the desired goal.
Photopurification of aqueous media using heterogeneous photosensitizers

A new materialconsisted of titanium dioxide microparticles photosensitized by organic dyes to generate photodynamic effect is suggested and tested. The effectivity of using such material for water photopurification from organic pollution using daylight is proven.
Balanced Binary Neural Networks with Gated Residual

Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt to maintain the information propagated in the forward process and propose a Balanced Binary Neural Networks with Gated Residual (BBG for short). First, a weight balanced binarization is introduced to maximize information entropy of binary weights, and thus the informative binary weights can capture more information contained in the activations. Second, for binary activations, a gated residual is further appended to compensate their information loss during the forward process, with a slight overhead. Both techniques can be wrapped as a generic network module that supports various network architectures for different tasks including classification and detection. We evaluate our BBG on image classification tasks over CIFAR-10/100 and ImageNet and on detection task over Pascal VOC. The experimental results show that BBG-Net performs remarkably well across various network architectures such as VGG, ResNet and SSD with the superior performance over state-of-the-art methods in terms of memory consumption, inference speed and accuracy.
Guides: Scholarly Communication & Institutional Repository: Data Management Services

Scholarly communication(SC) is the process by which academics, scholars, and researchers share and publish their research findings so that they are available to the wider academic community and beyond.
PENDIDIKAN BERBASIS TEKNOLOGI

The purpose of making this paper is to find out the meaning of Technology-Based Education the benefits of technology in the world of Education, as well as knowing the obstacles to the application of technology-based education. Technology is one of the keys to making education more prosperous. With technology-based education greatly influences the development of education both in the learning process and in the preparation of the curriculum, especially in building adequate educational facilities and infrastructure so that educational goals can be implemented. students to learn and get the information we need from anywhere, anytime, and from anyone. Constraints on the use of technology in schools include limited budgets for limited human resource learning facilities, hard work and lack of teacher competencies. Some benefits of the application of puzzleology in education, technology can help teachers teach, technology will trigger teacher creativity, help students learn, puzzleology can create learning activities that attract students easy to find learning resources, technology can improve school standards, make students have a broad insight
The Stroke Correspondence Problem, Revisited

We revisit the stroke correspondence problem [13,14]. We optimize this algorithm by 1) evaluating suitable preprocessing (normalization) methods 2) extending the algorithm with an additional distance measure to handle Hiragana, Katakana and Kanji characters with a low number of strokes and c) simplify the stroke linking algorithms. Our contributions are implemented in the free, open-source library ctegaki and in the demo-tools jTegaki and Kanjicanvas.
Corrections to “An Efficient and Effective Approach for Georeferencing AVHRR and GaoFen-1 Imageries Using Inland Water Bodies”

Presents corrections to above named paper.
Conditional Transferring Features: Scaling GANs to Thousands of Classes with 30% Less High-quality Data for Training

Generative adversarial network (GAN) has greatly improved the quality of unsupervised image generation. Previous GAN-based methods often require a large amount of high-quality training data while producing a small number (e.g., tens) of classes. This work aims to scale up GANs to thousands of classes meanwhile reducing the use of high-quality data in training. We propose an image generation method based on conditional transferring features, which can capture pixel-level semantic changes when transforming low-quality images into high-quality ones. Moreover, self-supervision learning is integrated into our GAN architecture to provide more label-free semantic supervisory information observed from the training data. As such, training our GAN architecture requires much fewer high-quality images with a small number of additional low-quality images. The experiments on CIFAR-10 and STL-10 show that even removing 30% high-quality images from the training set, our method can still outperform previous ones. The scalability on object classes has been experimentally validated: our method with 30% fewer high-quality images obtains the best quality in generating 1,000 ImageNet classes, as well as generating all 3,755 classes of CASIA-HWDB1.0 Chinese handwriting characters.
An agglomerative hierarchical clustering method by optimizing the average silhouette width

An agglomerative hierarchical clustering (AHC) framework and algorithm named HOSil based on a new linkage metric optimized by the average silhouette width (ASW) index is proposed. A conscientious investigation of various clustering methods and estimation indices is conducted across a diverse verities of data structures for three aims: a) clustering quality, b) clustering recovery, and c) estimation of number of clusters. HOSil has shown better clustering quality for a range of artificial and real world data structures as compared to k-means, PAM, single, complete, average, Ward, McQuitty, spectral, model-based, and several estimation methods. It can identify clusters of various shapes including spherical, elongated, relatively small sized clusters, clusters coming from different distributions including uniform, t, gamma and others. HOSil has shown good recovery for correct determination of the number of clusters. For some data structures only HOSil was able to identify the correct number of clusters.
Another Perspective on Cognitive Reading Strategy Use: Efficient and Inefficient Readers

This research investigated cognitive reading strategies used among university students for both efficient and inefficient readers, in southern Thailand. The study was conducted at two government universities. The data were collected through two research instruments: questionnaires and interviews. The questionnaires were distributed to 377 university students in March 2015 and the semi-structured interviews were implemented with 20 students, 10 efficient and 10 inefficient readers, afterward to accompany the quantitative data. The results from the questionnaire were analyzed and presented based on their descriptive statistics. The results revealed the percentages of cognitive reading strategies used between the two reading groups, also an independent t-test to check if there was any significant difference between efficient and inefficient readers on their strategy use. Next, the quantitative data were accompanied with discussion and explanation from the interview data. The findings verified that efficient readers tend to employ more strategies than inefficient readers when they read. Nevertheless, both reading groups were found to lack the same strategy: summary writing, which is believed to be another important cognitive reading strategy that could enhance students’ reading comprehension. Therefore, this study urges the integration of reading strategies in the classroom as an intervention for students to understand and use with English readings texts.
멀티미디어 기반 ‘자기조절학습(self-regulated learning)’이 한국어 학습자의 어휘 학습 과정에 미치는 영향 - 중국인 중·고급 학습자의 사례를 중심으로 -

This study investigated how the use of context-based learning with multimedia tools affected second-language (L2) learners’ acquisition of Korean vocabulary. The recently developed Motion Dictionary (MD) is a video-based dictionary that searches an entire video library (e.g., TV, drama, movies) and returns every video clip related to a specified word. The MD was initially developed to provide students with relatable examples of vocabulary usage that are rich in context and culture. In this study, we focused on Chinese learners of L2 Korean who used self-regulated learning with three different multimedia variants: videos, information quantity, and meta-cognition.In regard to videos, we found that effect on vocabulary learning varied depending on whether viewers were familiar with a video or not; those who were familiar with a video displayed the characteristics of vividly remembering the scenes and lines they saw while studying, even after the end of the learning period. Next, with regard to quantity of information, all the learners who participated in the study were interested in what was provided by all the multimedia learning tools, but in some cases, it was difficult for them to focus on it. We pointed out that these problems were caused by the way information is presented rather than by the amount of information. We could also find interesting examples such as: If the learner is not provided with the relevant information, or if the information they want is not presented, the motivation for learning will rather increase, and the learners will find the dictionary. Finally, with respect to meta-cognition, we found that significant learning occurred, as well as increased learning motivation, when the background knowledge of the learners enhanced but also differed from the new information provided during the learning process. Further studies should be conducted with groups of participants with different demographics to further the discussion of self-regulated learning courses based on multimedia learning tools in Korean language education.
Rules Enforcement And Unethical Behavior: Empirical research on Employee Fraud in Local Government Agencies

The tendency of fraud is one of the major threats to Indonesian country. The current research aims to study the effect of rules enforcement and unethical behaviors on the tendency of fraud based on gender. The sample consists of 126 employees from 28 Local Government Agencies (LGAs) in Kulon Progo regency. Purposive sampling was the sampling technique employed in this study. The results demonstrated a significant effect of unethical behavior on the tendency of fraud. However, rules enforcement level has no significant effect on the tendency of fraud. In addition, gender differences were observed in unethical behavior and the tendency of fraud. On the other hand, gender differences were not present in the level of rules enforcement.