Reviewerly/exHarmony huggingface dataset:
Tingkat kesiapan Perpustakaan Universitas Gadjah Mada dalam penerapan summon discovery service

Introduction. The information retrieval service continues to experience growth form of a card catalog into a computer-based catalog (OPAC), and became discovery. The purpose of this study to determine the level of readiness of UGM Library in the application of summon discovery service.Data Collection Method. This paper used survey with a quantitative approach. Analysis Data. The data was analyzed by statistical descriptive and hypothesis testing using SPSS software.Results and Discussions. The results showed of the organizational readiness of 2.55, which categorized “ready”The level of readiness of organizational resources obtained the score 2.54 and categorised “ready”.Conclusions.. In the variable organization, the highest indicator was organizational culture followed by the organizational structure and management support. In terms of organization Resources, human resources were the highest followed by ICT, and financial resources.
Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis

Teknik pemisahan foreground dari background pada citra statis merupakan penelitian yang sangat diperlukan dalam computer vision. Teknik yang sering digunakan adalah image segmentation, namun hasil ekstraksinya masih kurang akurat. Image matting menjadi salah satu solusi untuk memperbaiki hasil dari image segmentation. Pada metode supervised, image matting membutuhkan scribbles atau trimap sebagai constraint yang berfungsi untuk melabeli daerah tersebut adalah foreground atau background. Pada makalah ini dibangun metode unsupervised dengan mengakuisisi foreground dan background sebagai constraint secara otomatis. Akuisisi background ditentukan dari varian nilai fitur DCT (Discrete Cosinus Transform) yang dikelompokkan menggunakan algoritme k-means. Untuk mengakuisisi foreground ditentukan dari subset hasil klaster fitur DCT dengan fitur edge detection. Hasil dari proses akuisisi foreground dan background tersebut dijadikan sebagai constraint. Perbedaan hasil dari penelitian diukur menggunakan MAE (Mean Absolute Error) dibandingkan dengan metode supervised matting maupun dengan metode unsupervised matting lainnya. Skor MAE dari hasil eksperimen menunjukkan bahwa nilai alpha matte yang dihasilkan mempunyai perbedaan 0,0336 serta selisih waktu proses 0,4 detik dibandingkan metode supervised matting. Seluruh data citra berasal dari citra yang telah digunakan para peneliti sebelumnyaAbstractThe technique of separating the foreground and the background from a still image is widely used in computer vision. Current research in this technique is image segmentation. However, the result of its extraction is considered inaccurate. Furthermore, image matting is one solution to improve the effect of image segmentation. Mostly, the matting process used scribbles or trimap as a constraint, which is done manually as called a supervised method. The contribution offered in this paper lies in the acquisition of foreground and background that will be used to build constraints automatically. Background acquisition is determined from the variant value of the DCT feature that is clustered using the k-means algorithm. Foreground acquisition is determined by a subset resulting from clustering DCT values with edge detection features. The results of the two stages will be used as an automatic constraint method. The success of the proposed method, the constraint will be used in the supervised matting method. The difference in results from In the research experiment was measured using MAE (Mean Absolute Error) compared with the supervised matting method and with other unsupervised matting methods. The MAE score from the experimental results shows that the alpha matte value produced has a difference of 0.336, and the difference in processing time is 0.4 seconds compared to the supervised matting method. All image data comes from images that have been used by previous researchers.
Brittainie,This is such a cool way to ...

Brittainie,This is such a cool way to do your annotation read post and you communicate so much. I might encourage this as a practice for
Detection of misclassified and adversarial examples in Deep Learning

Great developments have been carried out thanks to deep learning in recent years. However, for a massive adoption in real-world applications, deep learning models need to be trustworthy. In this work we identify scenarios where such models behave in an unexpected fashion, implement a method based on the entropy for assessing their reliability and build a deep learning-based system able to detect its own wrong predictions. Additionally, by using the mentioned method, we achieve a relevant improvement of accuracy with an ensemble of learners.
Improved Document Modelling with a Neural Discourse Parser

Despite the success of attention-based neural models for natural language generation and classification tasks, they are unable to capture the discourse structure of larger documents. We hypothesize that explicit discourse representations have utility for NLP tasks over longer documents or document sequences, which sequence-to-sequence models are unable to capture. For abstractive summarization, for instance, conventional neural models simply match source documents and the summary in a latent space without explicit representation of text structure or relations. In this paper, we propose to use neural discourse representations obtained from a rhetorical structure theory (RST) parser to enhance document representations. Specifically, document representations are generated for discourse spans, known as the elementary discourse units (EDUs). We empirically investigate the benefit of the proposed approach on two different tasks: abstractive summarization and popularity prediction of online petitions. We find that the proposed approach leads to improvements in all cases.
Partial separability/entanglement violates distributive rules

We found three qubit Greenberger-Horne-Zeilinger diagonal states which tells us that the partial separability of three qubit states violates the distributive rules with respect to the two operations of convex sum and intersection. The gaps between the convex sets involving the distributive rules are of nonzero volume.
Modular Inference of Linear Types for Multiplicity-Annotated Arrows

Bernardy et al. [2018] proposed a linear type system $\lambda^q_\to$ as a core type system of Linear Haskell. In the system, linearity is represented by annotated arrow types $A \to_m B$, where $m$ denotes the multiplicity of the argument. Thanks to this representation, existing non-linear code typechecks as it is, and newly written linear code can be used with existing non-linear code in many cases. However, little is known about the type inference of $\lambda^q_\to$. Although the Linear Haskell implementation is equipped with type inference, its algorithm has not been formalized, and the implementation often fails to infer principal types, especially for higher-order functions. In this paper, based on OutsideIn(X) [Vytiniotis et al., 2011], we propose an inference system for a rank 1 qualified-typed variant of $\lambda^q_\to$, which infers principal types. A technical challenge in this new setting is to deal with ambiguous types inferred by naive qualified typing. We address this ambiguity issue through quantifier elimination and demonstrate the effectiveness of the approach with examples.
Changes in Security at BCS

Abstract Ian Fish FBCS and Chair of the Information Security Specialist Group, invites members to get in touch with the reformed Security and Privacy Executive.
Credibility Assessment of Simulation Models Using Hesitant Cloud Linguistic Term Sets

Credibility assessment of a simulation model is a hard problem as subjectivity is always involved. Linguistic expressions are much more straightforward and friendlier for a subject matter expert (SME) to represent qualitative judgments. Therefore, an assessment method based on hesitant cloud linguistic term sets (HCLTSs) is proposed in this paper. SMEs use context-free grammars and linguistic expressions to express their opinions. Then, these linguistic expressions are transformed into HCLTSs. Finally, aggregation results are calculated by synthetic cloud algorithm and weighted average cloud algorithm. An example under multiple-criteria group decision making is presented to illustrate the proposed method for credibility assessment.
Visualising the Training Process of Convolutional Neural Networks for Non-Experts

Convolutional neural networks are very complex and not easily interpretable by humans. Several tools give more insight into the training process and decision making of neural networks but are not un- derstandable for people with no or limited knowledge about artificial neural networks. Since these non-experts sometimes do need to rely on the decisions of a neural network, we developed an open-source tool that intuitively visualises the training process of a neural network. We visualize neuron activity using the dimensionality reduction method UMAP. By plotting neuron activity after every epoch, we create a video that shows how the neural network improves itself throughout the training phase. We evaluated our method by analysing the visualization on a CNN training on a sketch data set. We show how a video of the training over time gives more insight than a static visualisation at the end of training, as well as which features are useful to visualise for non-experts. We conclude that most of the useful deductions made from the videos are suitable for non-experts, which indicates that the visualization tool might be helpful in practice.
중국과 한국 설화를 활용한 한국 문화 교재 개발 방안 연구

In this article, we tried to find out the principle of development of Korean cultural textbooks from the viewpoint of cultural comparison by comparing two similar folktales between China and Korea. And we searched for the method of unit composition of cultural textbooks. Because folktales are familiar to learners and useful for stimulating learners'' interest and background knowledge, it would be useful for students'' learning of Korean culture. In addition, learners can naturally learn their own culture and Korean culture objectively by comparing the similarities and differences in the stories with similar motifs. With this background in mind, we have looked at the following points in this paper. First of all, we suggested to select and use Chinese folktales that are familiar to Chinese learners. It is to find out the universality and specificity of the culture inherited in the narrative by contrasting it with the similar folktales those of Korean narrative. The contents of the cultural education are mainly related to the perception culture of the Koreans, and the cultural exercise activities suggested various things in connection with speaking and writing. Next, we developed a model of unit composition of Korean cultural textbooks that reflect the principles of development of Korean cultural textbooks. Finally, based on these discussions, we developed an actual unit by exemplifying the Chinese tale and the Korean tale .(Inje Univ. · Dong-A Univ.)
Domain-Aware No-Reference Image Quality Assessment.

Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and then used as the unique characteristics to identify the degradation sources and assess the quality of the image. To this end, we design a new domain-aware architecture, which enables simultaneous determination of both the distortion sources and the quality of an image. With the distortion in an image better characterized, the image quality can be more accurately assessed, as verified by extensive experiments, which show that the proposed DA-IQA performs better than almost all the compared state-of-the-art NR-IQA methods.
The Effect of OK4R (Overview, Key ideas, Read, Recite, Review, Reflect) method on students' reading comprehension

The study is aimed at responding the following question: Is the student achievement in reading comprehension better taught by applying OK4R than conventional method. The subject of this study were 30 students at X-IIS 1 of SMA Cerdas Murni. The finding research shows that: The students that were taught by OK4R method have higher score than students were taught by conventional method. It was proved from the result of tobservation (5.533) > ttable (1.671). It shows that students got better achievement in comprehend the text. The researcher found that there was significant achievement of the students’ that were taught by applying OK4R method. It means that the students’ were taught by OK4R method was better than taught by conventional method. The analysis of requirement test used normality and homogeneity test, and analyzing data used t-test formula. After analyzing the data, the writer got; From the data it is seen that the students’ achievement in comprehend the text by applying OK4R method is higher than by conventional method at the tenth grade of SMA CERDAS MURNI. The students’ achievement in reading comprehension by applying OK4R method got the mean 76.833 and standard deviation is 5.331. The students’ achievement in reading comprehension who were taught conventionally got the mean 70.500 and standard deviation is 3.308. It means OK4R method is significant to be used to increasing students’ reading comprehension. The result of tobservation is 5.533 and ttable is 1.671 (tobservation > ttable , 5.533 > 1.671). That shows if Ho is rejected and Ha is accepted. There is significant effect of OK4R method on students’ reading comprehension.
Rethinking Generalisation

In this paper, a new approach to computing the generalisation performance is presented that assumes the distribution of risks, $ρ(r)$, for a learning scenario is known. From this, the expected error of a learning machine using empirical risk minimisation is computed for both classification and regression problems. A critical quantity in determining the generalisation performance is the power-law behaviour of $ρ(r)$ around its minimum value---a quantity we call attunement. The distribution $ρ(r)$ is computed for the case of all Boolean functions and for the perceptron used in two different problem settings. Initially a simplified analysis is presented where an independence assumption about the losses is made. A more accurate analysis is carried out taking into account chance correlations in the training set. This leads to corrections in the typical behaviour that is observed.
Penerapan Algoritma Naive Bayes pada Penentuan Kelayakan Calon Tenaga Kerja Indonesia

Indonesian Manpower is a potential that is a huge potential for the progress of the country. However, the difficulty of employment and the high unemployment rate in Indonesia requires that some people seek perfect employment abroad, in order to improve economic levels. The lack of selection resulted in many problems in the workforce, the low level of education of prospective migrant workers resulted in them having an easy risk on other party tricks, non-violence, unpaid salaries and so on. In accordance with what has been surveyed, it turns out that the sending of these workers is actually not feasible, given the level of education, skills and abilities that are lacking for employment abroad. This study aims to facilitate the government or companies engaged in the field to channel selected workers using the Naive Bayes Method.
MENANAMKAN NILAI KEIMANAN DENGAN METODE BERMAIN PERAN TOKOH AGAMA DALAM UPAYA MENINGKATKAN PRESTASI BELAJAR AGAMA HINDU

The purpose of conducting this class action research is to improve the learning achievement of students of class IX-4 in semester I of the 2018/2019 academic year at SMP Negeri 6 Denpasar. This research was conducted on 41 research subjects. The way to do this is to add old conventional learning styles into new learning methods that are discovery using the use of the question have strategy (questions raised by students themselves). The study was conducted in two cycles, each cycle carried out with the stages of planning, implementation, observation and reflection. To collect data on the results of research used learning achievement tests and to analyze the data used descriptive analysis. Judging from the results of evaluations that have been carried out an increase from the initial data with an average of 76.12 with mastery learning 36.59%, in the first cycle the average increased to 79.51 with mastery learning 75.61% and in the second cycle increased again to 83.54 with 100% mastery learning. Thus it can be concluded that the use of the question have strategy (questions raised by students themselves) can improve learning achievement in English.
The Effect of Education on Democracy: Panel Data Analysis of High Income Countries

The aim of this study is to determine the effect of education on democracy.Regions having developed democracy are the centers of attraction that individuals prefer to live in.From this point of view, the whole society's benefitting from individual freedom and equality, which are the basic building blocks of democracy, are closely related to the existence of educated individuals who make up that democratic society.Therefore, it can be stated that societies with a high level of education are of a high level of democracy.For this purpose, panel data method was used to measure the effect of education on democracy for high income country group in the period of 1990 and 2015 and the effect of education on democracy was investigated.According to the findings, it was found out that education had a positive effect on democracy.Since the selected country group has a high level of education, the finding reveals the importance of educated individuals for advanced democracy.Therefore, the result of this study supports the view that one of the factors that have a high level of development in high-income countries compared to other advanced democratic also contributes to this study.
Regeneration and Joining of the Learned Motion Primitives for Automated Vehicle Motion Planning Applications

How to integrate human factors into the motion planning system is of great significance for improving the acceptance of intelligent vehicles. Decomposing motion into primitives and then accurately and smoothly joining the motion primitives (MPs) is an essential issue in the motion planning system. Therefore, the purpose of this paper is to regenerate and join the learned MPs in the library. By applying a representation algorithm based on the modified dynamic movement primitives (DMPs) and singular value decomposition (SVD), our method separates the basic shape parameters and fine-tuning shape parameters from the same type of demonstration trajectories in the MP library. Moreover, we convert the MP joining problem into a re-representation problem and use the characteristics of the proposed representation algorithm to achieve an accurate and smooth transition. This paper demonstrates that the proposed method can effectively reduce the number of shape adjustment parameters when the MPs are regenerated without affecting the accuracy of the representation. Besides, we also present the ability of the proposed method to smooth the velocity jump when the MPs are connected and evaluate its effect on the accuracy of tracking the set target points. The results show that the proposed method can not only improve the adjustment ability of a single MP in response to different motion planning requirements but also meet the basic requirements of MP joining in the generation of MP sequences.
Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images.

The representation of objects as 2D bounding boxes in monocular RGB images limits the faculty of current computer vision systems to 2D object detection. It fails to provide crucial information such as the orientation of other vehicles, which is vital for autonomous driving. At the same time, real-time performance is essential to qualify an approach for deployment in a productive environment. In order to tackle this problem, we present an approach that predicts several key points selected from a virtual 3D bounding box around a vehicle instead of a pure 2D bounding box. These key points can be interpreted as a bounding shape. With this novel representation we can calculate the actual 3D bounding box of the corresponding object. Thanks to the straightforward implementation of bounding shape in any current state-of-the-art 2D object detector both for singleshot frameworks like YOLO or SSD as well as for two-stage detectors like Faster-RCNN with a minimum of computational overhead, it is able to be run in real-time while providing additional useful information for vehicle detection. We exemplify the extension of SSD to Bounding Shape SSD ( $\text{BS}^{3}\mathrm{D}$ ) and evaluate our approach using the challenging KITTI as well as the novel VIPER dataset.