Reviewerly/exHarmony huggingface dataset:
PELATIHAN PEMBUATAN MEDIA FLIPBOOK BAGI GURU SMA MUHAMMADIYAH SE-DKI JAKARTA

The service activities carried is training in making flipbook media for teachers in Muhammadiyah High Schools at DKI Jakarta. The partners of the service activities is Dikdasmen PWM DKI Jakarta. Based on the survey, interviews with the vice headmaster and the Secretary of the Primary and Secondary Education that there had never been a learning media training, especially flipbook and as many as 75% of teachers had never used flipbook media in learning. Based on these problems, the service activities offered offer comprehensive training starting from an understanding of media literacy, the importance of learning media, providing material about flipbook media, how to make flipbook media in accordance with the steps described and how to use flipbook media in learning. The service activity was held in January 2019. The target of the service activities was in the form of services and publications. The service coverage is in the form of material exposure regarding through flipbook media creation training. The steps taken in this service activity are : 1) Explained of material about media literacy, 2) Explained of material about learning media, 3) Explained of material about flipbook media, 4) Making flipbook media, 5) Application of flipbook media.Keywords: Education, learning media, flipbook
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on particle and multiwalker robot environments that have characteristics such as system health, risk-taking, semi-expendable agents, continuous action spaces, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
Full-Disk Solar Flare Forecasting Model Based on Data Mining Method

Solar flare is one of the violent solar eruptive phenomena; many solar flare forecasting models are built based on the properties of active regions. However, most of these models only focus on active regions within 30° of solar disk center because of the projection effect. Using cost sensitive decision tree algorithm, we build two solar flare forecasting models from the active regions within 30° of solar disk center and outside 30° of solar disk center, respectively. The performances of these two models are compared and analyzed. Merging these two models into a single one, we obtain a full-disk solar flare forecasting model.
Towards Robust Relational Causal Discovery

We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based conditional independence (CI) tests yield reliable answers to RCI queries against relational data. We show how to conduct CI tests against relational data to robustly recover the underlying relational causal structure. Results of our experiments demonstrate the effectiveness of our proposed approach.
Adversarial Self-Defense for Cycle-Consistent GANs

The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains. State-of-art methods learn the correspondence using large numbers of unpaired examples from both domains and are based on generative adversarial networks. In order to preserve the semantics of the input image, the adversarial objective is usually combined with a cycle-consistency loss that penalizes incorrect reconstruction of the input image from the translated one. However, if the target mapping is many-to-one, e.g. aerial photos to maps, such a restriction forces the generator to hide information in low-amplitude structured noise that is undetectable by human eye or by the discriminator. In this paper, we show how such self-attacking behavior of unsupervised translation methods affects their performance and provide two defense techniques. We perform a quantitative evaluation of the proposed techniques and show that making the translation model more robust to the self-adversarial attack increases its generation quality and reconstruction reliability and makes the model less sensitive to low-amplitude perturbations.
The effect of preexposure on overall similarity categorization.

This paper examines the effect that prior exposure to perceptual stimuli has on the prevalence of overall similarity (family resemblance) categorization.Experiment 1 demonstrated that participants who had previously encountered stimuli produced more overall similarity sorting when asked to free classify them than participants who were pre-exposed to different stimuli to those they later classified.Experiments 2a and 2b showed that this effect is modulated by the perceptual difficulty of the stimuli -pre-exposure statistically increased overall similarity sorting for perceptually easy stimuli but not for perceptually difficult stimuli.Overall similarity sorting was also significantly higher for perceptually easy stimuli than for perceptually difficult stimuli.Experiment 2b additionally showed that pre-exposure increased the discriminability of the perceptually easy stimuli but this effect was not statistically detectable for perceptually difficult stimuli.Experiment 3 established that the preexposure effect is also influenced by the spatial separateness of the stimulus dimensions -pre-exposure significantly elevated overall similarity sorting when the dimensions were integrated into a coherent object but not when they were spatially separated.Similarly, there was a statistically significant increase in the perceptual discriminability of the spatially integrated stimuli after pre-exposure but not for the spatially separate stimuli.Taken together, these results demonstrate that pre-exposure can elevate overall similarity sorting and provide insight into the conditions under which the effect will occur.
Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions

To automatically analyse complex trajectory information enclosed in clinical text (e.g.timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time.In the clinical domain, few temporally annotated corpora are currently available.Moreover, underlying annotation schemas -which mainly rely on the TimeML standard -are not necessarily easily applicable for applications such as patient timeline reconstruction.In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML.The developed corpus is available to the NLP community.Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions.As a result, we propose a novel annotation schema that could be useful for timeline reconstruction: CALendar EXpression (CALEX).
Peningkatan Penguasaan Konsep “Kebijakan Fiskal dan Moneter” Melalui Model Pembelajaran Mandiri Aktif Bermedia Audio Visual pada Siswa Kelas XI-1 SMA Negeri 4 Kediri

This research is a type of classroom action research using a collaborative model with 4 steps in each action. The object of action is the application of an active self-directed audio media learning model is VCD. The action has been done in 2 cycles with the research subject class XI-1 SMA Negeri 4 Kediri. The formulation of the problem includes: 1) How are the steps of the teacher in applying the concept of active self-learning in audio visual media. 2) How are the steps of the teacher in increasing student involvement in teaching and learning activities. 3) Whether the application of the concept of active self-learning with audio visual media can increase students mastery of the concept of “fiscal and monetary policy”. The conclusion is: 1) From the teacher’s point of view, steps have been found to apply the concept of active self-learning with audio visual/VCD media to improve the mastery of the concepts of “fiscal and monetary policy”. 2) From the teacher’s point of view, steps have been found to apply the concept of active self-learning with audio visual/VCD media to increase student involvement in teaching and learning activities. 3) From the student’s point of view, it has been successfully proven that the application of the concept of of active self-learning with audio visual/VCD media to improve the mastery of the concepts of “fiscal and monetary policy”.
Karakteristik struktur dan kebahasaan Teks Eksposisi karya Mahasiswa Fakultas Bahasa dan Seni Universitas Negeri Padang

The purpose of this research is describe the two following points, first is to describe structural characteristic of text exposition student work, second is to desribe linguistic exposition. The students are PPBSI JBSID FBS UNP. Type ofthis research is qualitative research with descriptive method. This research is done at UNP. The data of this research are the exposition texts which were got from informant. There are 20 exposition text of student work. The isntrumen of this researcher it self, and the technique of data analysis are by describing the data, analyzing and discussion. The result showed that there are there characteristic of text exposition; thesis, argument and the confirmation repetition argumen. And also there are five characteristic of linguistics found on student work; pronomina,nomina, verba, adjevtive and adverb. Based on the research it can be concluded that there are two important things, first in writing exposition text the student using there exposition characteristic. It can be said that all of them using all character, it can be proved that from 20 text, there are 12 text that using thesis, argument and confirmation repetition argument. Second, the student using linguistic character in writing text expoition. The mostly character that used is nomina.
Pointer Tagging for Memory Safety

Memory safety attacks continue to be prevalent on computer systems in use today, as large amounts of unsafe C/C++ code continues to provide attackers with a large supply of buffer overrun, use after free and type confusion bugs. This paper proposes a fundamental instruction set architecture change to combat memory safety problems. The ISA change is mostly transparent to application code and typically only requires a recompilation of the application to gain the security benefits. The change involves having the CPU hold two extra tag bits to the side of each piece of 64-bit data to denote whether the data holds a code/data pointer or not. By doing this, we can prevent attackers from using ‘data’ to corrupt ‘pointers’ and cause undesired damage. We believe the proposed architecture change enables stronger control flow protection than shadow stack (Intel CET) plus Control Flow Guard (CFG) with less performance overhead. Thus, this ISA change not only enhances security, but does so while improving performance.
A New Object Scene Flow Algorithm Based on Support Points Selection and Robust Moving Object Proposal

Recent algorithms of object scene flow estimation suffer from low computational efficiency or unstable moving object proposals. To tackle these two problems simultaneously, in this paper we propose a new, efficient and robust algorithm for object scene flow estimation, through making two technical contributions. Firstly to improve the efficiency, we propose to select only a few pixels termed support points for matching cost calculation rather than using all pixels. The support points are defined as those pixels with high confidence in feature matching. Secondly to attain stable moving object proposals, we propose a motion magnitude-adaptive thresholding scheme for ego-motion outlier detection, after patch matching on CNN-extracted high quality features. These two contributions, though simple, ensure a remarkable improvement in both efficiency and accuracy from the original object scene flow method, as well as making the proposed algorithm a strong practicable alternative to much more sophisticated state-of-the-art competitors.
Organizational and Managerial Mechanism of Professional Orientation at Early Stages of Life of Human Training

The successful introduction of an organisational and managerial mechanism in the form of a “school-university" model makes a significant contribution to the success of the implementation of vocational guidance projects. The contemporary education system of Russia includes theformation and organisation of the schoolchild’s vocational orientation, albeit at a difficult stage of development. It is due both to the transformational processes in education and the influence of numerous economic, social and other factors. Under market conditions, one of the topical issues for education authorities is the formation of an organisational mechanism for vocational guidance, aimed at ensuring the effective functioning of the vocational guidance system. The purpose of our study is to clarify the explanation of the working of the organisational and managerial mechanism of vocational guidance in the early stages of human life and training. The object of research was the process of vocational guidance of senior pupils of a general education school. The subject of the research was methodological approaches to the management of the vocational guidance process in the “school-university" model. In the study, we used general scientific methods of scientific continuity, comparisons, observation, dialectical logic, epistemological, logical analysis and modelling. As a result of a theoretical study, we determined the essence of the schoolchild’s vocational orientation and identified the need to create a mechanism for managing it in the context of the implementation of innovative approaches in vocational guidance. The organisational and managerial mechanism for professional orientation we presented in the form of a set of blocks. They include the main elements of the management system and the necessary links between these elements. We also outlined the guidelines for improving the management mechanism and the development of a vocational guidance system based on the project approach.
Why Do Public Blockchains Need Formal and Effective Internal Governance Mechanisms?

With the birth and rise of cryptocurrencies following the success of Bitcoin and the popularity of 'Initial Coin Offerings,' public awareness of blockchain technologies has substantially increased in recent years. Many blockchain advocates claim that these software artefacts enable radically new forms of decentralised governance by relying upon computational trust created via cryptographic proof, obviating the need for reliance on conventional trusted third-party intermediaries. But these claims rest on some key assumptions, which this paper subjects to critical examination. It asks: can existing mechanisms and procedures for collective decision-making of public blockchains (which we refer to as internal blockchain governance) live up to these ambitions? By drawing upon HLA Hart's Concept of Law, together with literature from regulatory governance studies, we argue that unless public blockchain systems establish formal and effective internal governance, they are unlikely to be taken up at scale as a tool for social coordination, and are thus likely to remain, at best, a marginal technology.
Deep Reinforcement Learning via Past-Success Directed Exploration

The balance between exploration and exploitation has always been a core challenge in reinforcement learning. This paper proposes “past-success exploration strategy combined with Softmax action selection”(PSE-Softmax) as an adaptive control method for taking advantage of the characteristics of the online learning process of the agent to adapt exploration parameters dynamically. The proposed strategy is tested on OpenAI Gym with discrete and continuous control tasks, and the experimental results show that PSE-Softmax strategy delivers better performance than deep reinforcement learning algorithms with basic exploration strategies.
Anticipatory computing: Crowd intelligence from social network and big data

This study investigated the role of three relationship properties embedded in organization-public relationships (OPR) built through social media interaction. We hypothesize that relationship strength, cohesion, and symmetry are positively related to three types of OPR, i.e., communal, exchange and symbiotic relationships. To test our hypotheses, we survey 73 nonprofit organizations and apply structural equation modelling to analyze hypothesized relationships. Results indicated that strong and cohesive interaction on social media is positively related to communal and symbiotic OPR, but not exchange OPR. On the other hand, symmetrical interaction is positively related to exchange OPR, but not communal and symbiotic OPR. Exchange OPR allows trading of benefits through social media interaction and this is positively related to symbiotic OPR. Understanding the association between the above relationship properties or patterns and OPR can assist organizations in developing strategic relational practices sufficient for organization-public interaction on social media.
Overview of the ImageCLEFmed 2019 concept detection task

This paper describes the ImageCLEF 2019 Concept Detection Task. This is the 3rd edition of the medical caption task, after it was first proposed in ImageCLEF 2017. Concept detection from medical images remains a challenging task. In 2019, the format changed to a single subtask and it is part of the medical tasks, alongside the tuberculosis and visual question and answering tasks. To reduce noisy labels and limit variety, the data set focuses solely on radiology images rather than biomedical figures, extracted from the biomedical open access literature (PubMed Central). The development data consists of 56,629 training and 14,157 validation images, with corresponding Unified Medical Language System (UMLSR) concepts, extracted from the image captions. In 2019 the participation is higher, regarding the number of participating teams as well as the number of submitted runs. Several approaches were used by the teams, mostly deep learning techniques. Long short-term memory (LSTM) recurrent neural networks (RNN), adversarial auto-encoder, convolutional neural networks (CNN) image encoders and transfer learning-based multi-label classification models were the frequently used approaches. Evaluation uses F1-scores computed per image and averaged across all 10,000 test images.
Advances in Cost-sensitive Multiclass and Multilabel Classification

Classification is an important problem for data mining and knowledge discovery and comes with a wide range of applications. Different applications usually evaluate the classification performance with different criteria. The variety of criteria calls for cost-sensitive classification algorithms, which take the specific criterion as input to the learning algorithm and adapt to different criteria more easily. While the cost-sensitive binary classification problem has been relatively well-studied, the cost-sensitive multiclass and multilabel classification problems are harder to solve because of the sophisticated nature of their evaluation criteria. The tutorial aims to review current techniques for solving cost-sensitive multiclass and multilabel classification problems, with the hope of helping more real-world applications enjoy the benefits of cost-sensitive classification.
Abstractive Document Summarization without Parallel Data

Abstractive summarization typically relies on large collections of paired articles and summaries. However, in many cases, parallel data is scarce and costly to obtain. We develop an abstractive summarization system that relies only on large collections of example summaries and non-matching articles. Our approach consists of an unsupervised sentence extractor that selects salient sentences to include in the final summary, as well as a sentence abstractor that is trained on pseudo-parallel and synthetic data, that paraphrases each of the extracted sentences. We perform an extensive evaluation of our method: on the CNN/DailyMail benchmark, on which we compare our approach to fully supervised baselines, as well as on the novel task of automatically generating a press release from a scientific journal article, which is well suited for our system. We show promising performance on both tasks, without relying on any article-summary pairs.
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract information from evidence without letting evidence communicate with each other, e.g., merely concatenate the evidence for processing. Therefore, these methods are unable to grasp sufficient relational and logical information among the evidence. To alleviate this issue, we propose a graph-based evidence aggregating and reasoning (GEAR) framework which enables information to transfer on a fully-connected evidence graph and then utilizes different aggregators to collect multi-evidence information. We further employ BERT, an effective pre-trained language representation model, to improve the performance. Experimental results on a large-scale benchmark dataset FEVER have demonstrated that GEAR could leverage multi-evidence information for FV and thus achieves the promising result with a test FEVER score of 67.10%. Our code is available at https://github.com/thunlp/GEAR.
Students’ and Their Parents’ Choice of Higher Education Institution in the Era of Digitalization

The relevance of the study is caused by great challenges facing the educational system. First of all, we mean the degree of compliance between the level and quality of training provided by the education system and real requirements of the labor market where the skillful knowledge of digital technologies comes to the fore. Important in this aspect is how applicants and their parents choose the university for training. In this regard, this article aims to analyze the issue of applicants’ and their parents’ choice in relation to higher education. The leading method for this study is the sociological method with the subsequent interpretation of results, that allows to make a comprehensive review of the structure of the main factors affecting the choice of an educational institution for training. Taking into account that parents of applicants play a significant role by the decision making process in this sphere, their position was also studied. The article presents results of the aggregated digital data obtained in the course of sociological research (questionnaire in the electronic form). The authors describe the most significant factors influencing the choice of an educational institution, as well as the main differences in relation to the factors affecting the choice of an educational institution by parents and applicants. The materials of the article are of practical value for employees of higher educational institutions, scientists, applicants, students and parents of future students.