Reviewerly/exHarmony huggingface dataset:
Uncertainty and certainty relations for quantum coherence with respect to design-structured POVMs

The concept of quantum coherence and its possible use as a resource are currently the subject of active researches. Uncertainty and complementarity relations for quantum coherence allow one to study its changes with respect to other characteristics of the process of interest. Protocols of quantum information processing often use measurements that have a special inner structure. Quantum designs are considered as tools with a perspective of fruitful applications in quantum information processing. We obtain uncertainty and certainty relations for coherence averaged with respect to a set of design-structured POVMs of rank one. To characterize the degree of coherence, the relative entropy of coherence is utilized. The derived relations are exemplified with quantum designs in the two-dimensional Hilbert space.
An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation

Background In the analysis of electronic health records, proper labeling of outcomes is mandatory. To obtain proper information from radiologic reports, several studies were conducted to classify radiologic reports using deep learning. However, the classification of pneumonia in bilingual radiologic reports has not been conducted previously. Objective The aim of this research was to classify radiologic reports into pneumonia or no pneumonia using a deep learning method. Methods A data set of radiology reports for chest computed tomography and chest x-rays of surgical patients from January 2008 to January 2018 in the Asan Medical Center in Korea was retrospectively analyzed. The classification performance of our long short-term memory (LSTM)–Attention model was compared with various deep learning and machine learning methods. The area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, sensitivity, specificity, accuracy, and F1 score for the models were compared. Results A total of 5450 radiologic reports were included that contained at least one pneumonia-related word. In the test set (n=1090), our proposed model showed 91.01% (992/1090) accuracy (AUROCs for negative, positive, and obscure were 0.98, 0.97, and 0.90, respectively). The top 3 performances of the models were based on FastText or LSTM. The convolutional neural network–based model showed a lower accuracy 73.03% (796/1090) than the other 2 algorithms. The classification of negative results had an F1 score of 0.96, whereas the classification of positive and uncertain results showed a lower performance (positive F1 score 0.83; uncertain F1 score 0.62). In the extra-validation set, our model showed 80.0% (642/803) accuracy (AUROCs for negative, positive, and obscure were 0.92, 0.96, and 0.84, respectively). Conclusions Our method showed excellent performance in classifying pneumonia in bilingual radiologic reports. The method could enrich the research on pneumonia by obtaining exact outcomes from electronic health data.
A Meta-Mining Ontology Framework for Data Processing

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.
DiCGAN: A Dilated Convolutional Generative Adversarial Network for Recommender Systems

Generative Adversarial Network (GAN) has recently been introduced into the domain of recommendation due to its ability of learning the distribution of users’ preferences. However, most existing GAN-based recommendation methods only exploit the user-item interactions, while ignoring to leverage the information between user’s interacted items. On the other hand, Convolutional Neural Network (CNN) has shown its power in learning high-order correlations. In this paper, combining with the strengths of both GAN and CNN, we propose a Dilated Convolutional Generative Adversarial Network (DiCGAN) for recommendation, in which we first embed the interacted items of per user into an image in a latent space, and then use several dilated convolutional filters and a vertical convolutional filter to capture the high-order correlations among the interacted items. Moreover, an attention module is employed before convolution to generate attention maps for adaptive feature refinement. Experiments on several public datasets verify the superiority of DiCGAN over several baselines in terms of top-N recommendation. Further more, our experimental results show that when the dataset is more large and sparse, the performance gain of DiCGAN is also more significant, demonstrating the effectiveness of the CNN component in extracting high-order correlations from interacted data for better performance.
ICT in teaching for children with intellectual disabilities

The impact of information and communication technologies (ICT) is increasing in teaching, and practice shows that there is a need for individual work in the educational process. This paper gives an analysis of the attitudes of teachers about the use of ICT as a means for individualizing teaching in regular and special primary schools. The results showed that teachers’ attitudes toward ICT as a way of individualization are positive, but they are more pronounced among teachers from special primary schools. Individualized teaching encourages students to progress, but it can negatively affect students’ social behavior. Teachers must have a good knowledge of working with ICT, so seminars on the use of ICT should be organized. ICT affect the education of all students. Teachers from both regular and special primary schools think agree that ICTs are just for fun and that they have no place in the classroom, but at home.
Google tech exchange

This paper describes Google Tech Exchange, an industry-academic partnership that involves several Historically Black Colleges and Hispanic Serving Institutions. Tech Exchange's mission is to unlock...
Compositional Game Theory with Mixed Strategies: Probabilistic Open Games Using a Distributive Law

We extend the open games framework for compositional game theory to encompass also mixed strategies, making essential use of the discrete probability distribution monad. We show that the resulting games form a symmetric monoidal category, which can be used to compose probabilistic games in parallel and sequentially. We also consider morphisms between games, and show that intuitive constructions give rise to functors and adjunctions between pure and probabilistic open games.
A Performance Evaluation Method of Load Balancing Capability in SaaS Layer of Cloud Platform

Abstract Cloud platform load balancing has become one of the essential elements in modern cloud platform operations. However, there are still some problems in the evaluation methods of cloud platform load balance, such as unreasonable evaluation indexes and lack of comprehensive evaluation methods. This paper first proposes a performance evaluation method of cloud platform load balancing capabilities. This method analyzes the characteristics of the cloud platform and the actual needs of users, designs and proposes a cloud platform load balancing capability evaluation system, quantitatively analyzes and quantifies specific indexes, and uses the Analytic Hierarchy Process to determine the weight of each index. Finally, we deploy and test our method on the OpenStack cloud platform. Experiments show that the evaluation results of the method proposed in this paper are consistent with the actual results, which verifies the feasibility and rationality of the method proposed in this paper.
When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting

In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task. However, the positional model is not practically fast and it has never been evaluated on qualitative criteria or extrinsic tasks. We propose a constrained positional model, which adapts the sparse attention mechanism from neural machine translation to improve the speed of the positional model. We evaluate the positional and constrained positional models on three novel qualitative criteria and on language modeling. We show that the positional and constrained positional models contain interpretable information about the grammatical properties of words and outperform other shallow models on language modeling. We also show that our constrained model outperforms the positional model on language modeling and trains twice as fast.
Selection of Representative Histologic Slides in Interobserver Reproducibility Studies: Insights from Expert Review for Ovarian Carcinoma Subtype Classification

Background: Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs). Materials and Methods: A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2–6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review). Results: Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance. Conclusions: The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.
Benefícios e dificuldades a partir da implementação do SPED: um estudo com profissionais de contabilidade.

The general objective of the study was to identify the benefits and difficulties coming from the implementation of SPED (Public System of Digital Bookkeeping) in the perception of accounting professionals in Fortaleza / CE. The quantitative research analyzed data collected through a survey applied to 140 accounting professionals in the city of Fortaleza - CE. For data analysis, descriptive statistics (measures of central tendency and dispersion) and exploratory factor analysis (EFA) were used. The results indicated that, among the benefits identified after the implementation of SPED, the most evidenced by accounting professionals in Fortaleza was the availability of quality information, while the most punctuated difficulty was the lack of knowledge about the software in the area. Thus, the relevance of SPED in accounting for information was verified. However, it is necessary that accounting professionals be in a process of constant improvement of their skills, including the ones in the technological area. In addition, these professionals, being aware of their difficulties in relation to SPED, can mitigate them and, consequently, contribute to society as they help to fulfill the tax requirements in relation to taxpayers.
Simulation-based inference with approximately correct parameters via maximum entropy

Abstract Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observed data fit with prior belief. The method requires the ansatz that data is fit in expectation, which is true in some settings and may be reasonable in all settings with few data points. The method is based on sample reweighting, so its asymptotic run time is independent of prior distribution dimension. We demonstrate this MaxEnt approach and compare with other likelihood-free inference methods across three systems: a point particle moving in a gravitational field, a compartmental model of epidemic spread and molecular dynamics simulation of a protein.
An Improved Similarity-Based Clustering Algorithm for Multi-Database Mining

Clustering algorithms for multi-database mining (MDM) rely on computing (n2−n)/2 pairwise similarities between n multiple databases to generate and evaluate m∈[1,(n2−n)/2] candidate clusterings in order to select the ideal partitioning that optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the n databases in one cluster or by returning n singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness of the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms, which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in fewer upper-bounded iterations. To achieve our goal, we use coordinate descent (CD) and back-propagation to search for the optimal clustering of the n multiple database in a way that minimizes a convex clustering quality measure L(θ) in less than (n2−n)/2 iterations. By using a max-heap data structure within our CD algorithm, we optimally choose the largest weight variable θp,q(i) at each iteration i such that taking the partial derivative of L(θ) with respect to θp,q(i) allows us to attain the next steepest descent minimizing L(θ) without using a learning rate. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.
BASBA: A framework for Building Adaptable Service-Based Applications

Due to the continuously changing environment of service-based applications (SBAs), the ability to adapt to environmental and contextual changes has become a crucial characteristic of such applications. Providing SBAs with this ability is a complex task, usually prepared in an unsystematic way and interwoven with application logic. As a result, developing and maintaining adaptive SBAs has become a costly and hardly repeatable process. The objective of this paper is to present a model-based approach to developing adaptive SBAs, which separates development of adaptation concerns from development of SBAs behaviors. This approach aims to facilitate and automate development of adaptive behaviors. In this paper, the process of developing an adaptive SBA is defined as specifying adaptive SBA models based on a metamodel and reusable adaptation tactics. These models are then transformed into runtime model artifacts and running system units performing runtime adaptive behaviors. The approach introduces a systematic method to derive adaptation behaviors from adaptation models, which facilitates the development of adaptive behaviors. The results show that using our approach can enhance the development process of adaptive behaviors in terms of development time and code quality, particularly for scenarios that are more complex in terms of adaptation rather than business logic.
Visual communication design: Poster as an important way to encourage social distance in Jakarta when the epidemic 19

Abstract Health campaigns especially regarding the COVID-19 epidemic need to be highlighted as dangerous viruses that cause fever, coughing, shortness of breath, chest pain and death. The health ministry wants the government to move public awareness in this direction. The research aims to examine the visual communication of a poster design to encourage social distancing due to the pandemic of COVID-19. This study has analyzed the key elements of health campaign posters in Jakarta. The poster remains becoming an active platform to inform display information, data, schedules or offers and to persuade people, causes, places, events, products, companies, services, groups or organizations in Jakarta. Previously the health poster campaign was not enough to attract parents’ attention to educate their children about having a healthy lifestyle. This study uses a qualitative approach to collect all the data needed. This includes case studies through visual analysis of local content and national poster design from a health campaign from the Jakarta health ministry. The results show that local and national poster designs have different elements of the design results that affect different posters. This result can be set as a guideline for the government in Jakarta to design a poster with good design aesthetic elements. It will also guide future graphic designers to improve the ability of their poster campaign.
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment

Designing robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order to create robots automatically, without the need for human intervention, it is necessary to optimise both the behaviour and the body design of the robot. However, when co-optimising the morphology and controller of a locomoting agent the morphology tends to converge prematurely, reaching a local optimum. Approaches such as explicit protection of morphological innovation have been used to reduce this problem, but it might also be possible to increase exploration of morphologies using a more indirect approach. We explore how changing the environment, where the agent locomotes, affects the convergence of morphologies. The agents' morphologies and controllers are co-optimised, while the environments the agents locomote in are evolved open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments. Our agents each contain of a population of individuals being evolved with a genetic algorithm. This population is called the agent-population. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments. POET proved capable of creating a curriculum of environments which encouraged both diversity and quality in the populations. This suggests that POET may be capable of reducing premature convergence in co-optimisation of morphology and controllers.
Pengembangan Media Belajar Literasi Digital Berbasis Game Edukasi untuk Siswa Kelas 2 SD

The purpose of this study is to increase the interest in reading from the problem of low interest in reading in grade 2 elementary students, with several factors of problems from low interest in reading and low reading skills of students are still lacking, the rise of types of entertainment such as playing games and watching TV so as to influence the attention of students choosing not to read. Researcher develop digital literacy learning media based on educational games by developing students to be able to play puzzles and read. With the media design created by Researcher, namely the collaboration between writing, images, and sounds, it is very important in communication and conveying messages to students, if students still do not know the word, they can see pictures and sounds that can help children in visualizing stories in reading. Researcher designed this media so that students can be interested and motivated and more enjoyable in digital literacy. Researcher used a type of development research (R&amp;D) and a qualitative descriptive approach to percentage. For data analysis techniques that have been conducted consists of preliminary studies, product development, validity tests and conclusion drawing. The results of this study after analysis of the data of the assessment of material expert validation test by 100% and media experts by 84.2%, with the results have been qualified and eligible to be tested after improvement according to the advice of experts.
A Pseudorandom Bit Generator Based on Sigmoid Function

In the literature, little attention is paid to designing and analyzing novel one dimensional chaotic mappings. In our previous efforts, we have tried a lot of unimodal mappings and hardly found any chaotic property for most of them. In this paper, besides translation & scale (already adopted before), we employ another elementary transformation, i.e. fold, to obtain a variant of Sigmoid Function. Both Bifurcation Diagram & Lyapunov Exponent Spectrum manifest that the new mapping possesses wonderful chaotic properties. Then, a pseudorandom bit generator is designed based on it. Pseudorandom tests demonstrate that the mapping is the 3rd best one so far. It owns great application prospect.