Reviewerly/exHarmony huggingface dataset:
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization

Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpretability, multi-task learning, and calibration. Because of the intractability of the resulting optimization problem, most BNNs are either sampled through Monte Carlo methods, or trained by minimizing a suitable Evidence Lower BOund (ELBO) on a variational approximation. In this paper, we propose a variant of the latter, wherein we replace the Kullback-Leibler divergence in the ELBO term with a Maximum Mean Discrepancy (MMD) estimator, inspired by recent work in variational inference. After motivating our proposal based on the properties of the MMD term, we proceed to show a number of empirical advantages of the proposed formulation over the state-of-the-art. In particular, our BNNs achieve higher accuracy on multiple benchmarks, including several image classification tasks. In addition, they are more robust to the selection of a prior over the weights, and they are better calibrated. As a second contribution, we provide a new formulation for estimating the uncertainty on a given prediction, showing it performs in a more robust fashion against adversarial attacks and the injection of noise over their inputs, compared to more classical criteria such as the differential entropy.
A Simple, Accurate and Highly Secure Method to Encrypt-Decrypt Digital Images

The digital image may be important and has a secret character, which requires not understanding it when looking at the naked eye or not understanding the contents. So seeking a method of digital image encryption-decryption is a very important task. In this paper we will introduce a new method of digital image encryption-decryption, which will be very simple, highly secure and accurate and highly efficient
Potensi Aplikasi belajar Biologi Berbasis Android dalam Meningkatkan Minat Belajar Siswa

Abstract:  Android is the most technology used by humans in this world. Utilization of android besides as medium of communication also as a learning medium known in mobile learning (m-learning) method. Mobile learning use media that are more owned by students from various conditions. Utilization of this technologi in learning to increase student learning outcomes. Student learning outcomes most depends of student interest in learning. Study of potential android based biology learning application aims to determine the ability of android-based biology learning application in increasing interst learning biology for students. The method used is literature review. The result of this study reveal that there was an increase in learning outcomes. So android based biology learning application has able to increase learning interest and learning outcomes. Keywords: biology learning application, android, learning interest of student
Mamdani Fuzzy Implementation for New Student Admission Selection

In the selection process for New Student Admissions, there are many prospective new students. However, not all candidates accepted at a school. They must be passing through the selection stage by following specific criteria. Therefore, schools need to select candidates following specific criteria to make the best selection of students. Especially in vocational high school (VHS), the selection processes are more complex. It is because every department in VHS certainly has its criteria (different in each department). Based on the background, this study aims to propose a method based on Mamdani Fuzzy that can be applied to the new student admission selection process. There are three main stages in this method, which are fuzzification, inference, and defuzzification.
E-books: Access vs Ownership

ABSTRACT Over the past several years, e-book ownership has become the preferred acquisitions model, as opposed to subscription models. Vendors and aggregators offer many different models to help streamline and simplify acquiring e-books, including front-file purchases from the vendor, as well as demand-driven/patron-driven acquisitions (DDA/PDA) and a variety of evidence-based plans. With each purchase model, though, come different technical services workflows required to manage not only the plans, but coordinate the purchasing, cataloging, and holdings maintenance. One must also consider the unpredictable costs per year, though deposit accounts make it easier to budget and manage the cost. Subscription resources, however, provide one workflow as well as a known cost component. In addition to this, the maintenance of the collection, including additions, deletions, and weeding out-of-date resources, is handled by the vendor. This article will discuss the pros and cons of both purchase and subscription models (including acquisitions, cataloging, and maintenance) and present the argument that, for small technical services departments, perhaps subscription is a better model than ownership, when available.
Linguistic images of modern parenthood on parenting websites

Contemporary socio-cultural transformations of family life can be observed in the area of non-specialist parental discourse in terms of both modern parenthood and the celebration of childhood. The new quality in the experience and creation of parenthood is noticeable especially in the communication performed with the help of Web 2.0 tools (which are currently among the most important platforms for the exchange of particularly valued “unscientific” – experience, because it is based on empirical knowledge). Internet messages (entries, posts, comments) create an involved parenting and glorify a small child as a subject of parental efforts in the context of post-hierarchical relationships, but they also serve to profile communities gathered around a superior idea or topic (e.g. eco-mum, modern mother, fitmum, dad in the city, dad at work, etc.). They also endorse the principle of the aestheticisation of everyday life, the messages are filled with linguistic humour and expose a non-standard way of thinking, matching perfectly the main trends of postmodern communication.
A Note on Latency Variability of Deep Neural Networks for Mobile Inference

Running deep neural network (DNN) inference on mobile devices, i.e., mobile inference, has become a growing trend, making inference less dependent on network connections and keeping private data locally. The prior studies on optimizing DNNs for mobile inference typically focus on the metric of average inference latency, thus implicitly assuming that mobile inference exhibits little latency variability. In this note, we conduct a preliminary measurement study on the latency variability of DNNs for mobile inference. We show that the inference latency variability can become quite significant in the presence of CPU resource contention. More interestingly, unlike the common belief that the relative performance superiority of DNNs on one device can carry over to another device and/or another level of resource contention, we highlight that a DNN model with a better latency performance than another model can become outperformed by the other model when resource contention be more severe or running on another device. Thus, when optimizing DNN models for mobile inference, only measuring the average latency may not be adequate; instead, latency variability under various conditions should be accounted for, including but not limited to different devices and different levels of CPU resource contention considered in this note.
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation

In this paper, we study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of empowering the discriminativeness: Adding a new, artificial class and training the model on the data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore drawing the decision boundaries more effectively. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.
Towards a Model For Performance Evaluation of Cloud Machines

In the past few years, cloud computing has emerged as an efficient and smart solution for various services, eliminating the need for physical computing hardware. With all these services provided by cloud computing, the market for cloud computing will increase in the coming years. There is a high chance of cloud inefficiency with the growth in usage. Moreover, the efficiency of virtual machines provided by cloud computing also depends on various factors. This paper attempts to come up with a model that could not only investigate the factors that impact the performance of machines in the cloud but also predict it. Various benchmarking tools and live experiments with a combination of physical and virtual machines led to a robust data-set that was used to generate the model. It was observed that the performance evaluation could be predicted based on several computational parameters.
Derivation of QUBO Formulations for Sparse Estimation

We propose a quadratic unconstrained binary optimization (QUBO) formulation of the l1-norm, which enables us to perform sparse estimation of Ising-type annealing methods such as quantum annealing. The QUBO formulation is derived using the Legendre transformation and the Wolfe theorem, which have recently been employed to derive the QUBO formulations of ReLU-type functions. It is shown that a simple application of the derivation method to the l1-norm case results in a redundant variable. Finally a simplified QUBO formulation is obtained by removing the redundant variable.
1DCAEAnalysis of Magnetic Brake and Its ExperimentalVerification

Recently, IoT and Industry 4.0 were very hot topics, especially in diagnosis and maintenance area. Because an advanced computer simulation technique could be utilized using a standard computer, many engineer paid attention to an computer simulation based diagnosis technique, i.e., a digital twin. In this paper, a physical computer model of a magnetic brake was build by modelica language and verified its performance by a experiment at a first step of a digital twin technique. A magnetic brake has multiphysics in it, so that its computer model is composed of a dynamics model, a magnetic model and a electrical model. A modelica language was suit for this type of modeling. Finally, the computer simulation showed very close correlation with experimental data.
Streaming Active Deep Forest for Evolving Data Stream Classification

In recent years, Deep Neural Networks (DNNs) have gained progressive momentum in many areas of machine learning. The layer-by-layer process of DNNs has inspired the development of many deep models, including deep ensembles. The most notable deep ensemble-based model is Deep Forest, which can achieve highly competitive performance while having much fewer hyper-parameters comparing to DNNs. In spite of its huge success in the batch learning setting, no effort has been made to adapt Deep Forest to the context of evolving data streams. In this work, we introduce the Streaming Deep Forest (SDF) algorithm, a high-performance deep ensemble method specially adapted to stream classification. We also present the Augmented Variable Uncertainty (AVU) active learning strategy to reduce the labeling cost in the streaming context. We compare the proposed methods to state-of-the-art streaming algorithms in a wide range of datasets. The results show that by following the AVU active learning strategy, SDF with only 70\% of labeling budget significantly outperforms other methods trained with all instances.
GECCO2019@Prague

The Genetic and Evolutionary Computation Conference (GECCO) took place on July 13th-17th 2019 in Prague, Czech Republic. This report provides statistics about submissions and authorship, and some comments about the evolution and growth of GECCO.
New Self-Dual Codes from 2x2 Block Circulant Matrices, Group Rings and Neighbours of Neighbours

In this paper, we construct self-dual codes from a construction that involves 2x2 block circulant matrices, group rings and a reverse circulant matrix. We provide conditions whereby this construction can yield self-dual codes. We construct self-dual codes of various lengths over F2, F2 + uF2 and F4 + uF4. Using extensions, neighbours and neighbours of neighbours, we construct 32 new self-dual codes of length 68.
A Fixed point view: A Model-Based Clustering Framework

With the inflation of the data, clustering analysis, as a branch of unsupervised learning, lacks unified understanding and application of its mathematical law. Based on the view of fixed point, this paper restates the model-based clustering and proposes a unified clustering framework. In order to find fixed points as cluster centers, the framework iteratively constructs the contraction map, which strongly reveals the convergence mechanism and interconnections among algorithms. By specifying a contraction map, Gaussian mixture model (GMM) can be mapped to the framework as an application. We hope the fixed point framework will help the design of future clustering algorithms.
Network with Sub-Networks

We introduce network with sub-networks, a neural network which its weight layers could be detached into sub-neural networks during inference. To develop weights and biases which could be inserted in both base and sub-neural networks, firstly, the parameters are copied from sub-model to base-model. Each model is forward-propagated separately. Gradients from a pair of networks are averaged and, used to update both networks. Our base model achieves the test-accuracy which is comparable to the regularly trained models, while the model maintains the ability to detach weight layers.
Finger Tracking Based Tabla Syllable Transcription

In this paper, a new visual-based automated tabla syllable transcription algorithm is proposed. The syllable played on the tabla depends on the manner in which various fingers strike the tabla. In the proposed approach, in first step fingers are tracked using SIFT features in all the frames. This path of fingers for various syllables are analyzed and rules are formulated. Then these rules are used to create a visual signature for different syllables. Finally, the visual signatures of all the frames are compared to the visual signature of the base frame and the frame is labeled to that syllable. Based on this the various signatures are classified into different syllables. Using the proposed method, we are able to transcript tabla syllables with 97.14% accuracy.
Evaluating Registration Without Ground Truth.

We present a generic method for assessing the quality of non-rigid registration (NRR) algorithms, that does not depend on the existence of any ground truth, but depends solely on the data itself. The data is a set of images. The output of any NRR of such a set of images is a dense correspondence across the whole set. Given such a dense correspondence, it is possible to build various generative statistical models of appearance variation across the set. We show that evaluating the quality of the registration can be mapped to the problem of evaluating the quality of the resultant statistical model. The quality of the model entails a comparison between the model and the image data that was used to construct it. It should be noted that this approach does not depend on the specifics of the registration algorithm used (i.e., whether a groupwise or pairwise algorithm was used to register the set of images), or on the specifics of the modelling approach used. We derive an index of image model specificity that can be used to assess image model quality, and hence the quality of registration. This approach is validated by comparing our assessment of registration quality with that derived from ground truth anatomical labeling. We demonstrate that our approach is capable of assessing NRR reliably without ground truth. Finally, to demonstrate the practicality of our method, different NRR algorithms -- both pairwise and groupwise -- are compared in terms of their performance on 3D MR brain data.
Semi-supervised Anomaly Detection on Attributed Graphs

We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independent and identically distributed, in many real-world applications, instances are often explicitly connected with each other, resulting in so-called attributed graphs. The proposed method embeds nodes (instances) on the attributed graph in the latent space by taking into account their attributes as well as the graph structure based on graph convolutional networks (GCNs). To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere. This enables us to detect anomalies by simply calculating the distances between the node embeddings and hypersphere center. The proposed method can effectively propagate label information on a small amount of nodes to unlabeled ones by taking into account the node's attributes, graph structure, and class imbalance. In experiments with five real-world attributed graph datasets, we demonstrate that the proposed method achieves better performance than various existing anomaly detection methods.
The Axiological Potential Of Scientific Knowledge In The Information Age

Introduction. The article is devoted to the study of the problem of the axiological potential of science. For more than 100 years, philosophy has been analyzing this problem, since the proposed solutions are not just different, but sometimes contradictory to each other. This is connected not only with the essence of science, but also with the understanding of values as the basis of culture. Scientific rationality does not arise from scratch, but the foundation of spiritual values already accumulated by society. As you know, society highly values all forms of social consciousness. But the place of science in culture is special, since only science is able to go beyond the scope of each concrete historical type of practice and show new subject areas to people. The aim and task. In society there are constantly changing values. This is also true for science. At the same time, science does not lose its status in culture, since the intellectualization of society is continued. Science teaches man to think, argue, create new. That is why it is necessary to analyze the causes of attempts to depreciate scientific activity and its results in the information society. Research methods. The methodological basis of the study is a set of philosophical and general scientific methods, which provides an analysis of the processes that take place in modern science and determine the directions of its further development. The article also applies systemic and synergistic approaches. Research results. Despite the successes of classical science, at the beginning of the 20th century, they began to accuse science of dehumanization. Obviously, social crises and wars undermine the authority of science. Throughout the 20th century, anti-scientist criticism has existed. At the same time, the image of science is changing to non-classical, and in the second half of the 20th century - to post-non-classical.Attention to the socio-cultural and historical dimensions of scientific knowledge allows us to re-realize the value potential of science. Discussion. In philosophical literature there are conflicting opinions about the value of science - from full exaltation to stating the "end" of science. The arguments of the opponents are interesting and deserve attention. Conclusions. The problem of the value potential of science is relevant not only in connection with the achievements of the scientific and technical sphere, but also with such phenomena as imitation of science and plagiarism. Taking into account the value potential of science contributes to its success and enhances the level of scientific culture in society as a whole.
Teachers’ revelations on self-motivation

The purpose of this study is to determine the level of teachers’ self-motivation. The study employed quantitative-descriptive research design. The participants are male and female teachers from three countries, Philippines, Vietnam, and the Sultanate of Oman, including nationals and non-nationals. The instrument used in gathering the data was sourced from an online article and slight modifications were made to suit the participants. A four-point qualitative scale was used to measure the self-motivation level of the participants and the weighted mean was used for interpreting the results. The study found that males’ self-motivation levels are higher than females’ as propelled by 1) self-confidence and self-efficacy; both male and female participants showed high motivation levels driven by 2) positive thinking and positive thinking about the future as well as 3) focus and strong goals; and 4) environmental factors inspired both groups of participants infrequently. In conclusion, the overall results showed that these four factors influenced self-motivation levels most of the time, and thus the teachers are highly motivated. A high level of self-motivation is below the exceptional level of self-motivation, and hence this implies that more push factors are necessary to facilitate remarkable and unique output. Incomparable self-motivation attitudes could lead to the attainment of the goals.
Computer-Supported Teaching Experiment of Sports Social Sciences

Based on pedagogy, cognitive psychology and the constructivist learning theory, this paper puts forward the computer-supported cognitive strategies for sports, and realizes the multi-interaction between a small group of the experts, the multimedia and the learners. At the same time, the questionnaire survey and the teaching experiment were designed and implemented. The results show that the computer-supported cooperative learning method effectively improves the students’ interests and skills in their sports learning, and is conducive to the cultivation of students’ cooperative abilities. The effectiveness of this method is related to the teaching strategies, and the group cooperative classroom synchronous teaching is the best. The computer-supported experimental research on the physical education and the teaching of social sciences has effectively improved participants’ theoretical literacy and the experimental teaching level, promoted teachers to actively carry out the experimental teaching exploration and research, and realized the deep integration of the modern educational equipment and the subject education and teaching.