Reviewerly/exHarmony huggingface dataset:
Looking at the new digital school: didactics 2.0 and school 2.0

Aim. Today we have the need to develop more effective teaching methods, where thestudent is no longer just a user, but becomes an integral part of the training process by creating,selecting and processing experiences in a personal way. Digital teaching has become/isbecoming part of the school scenario along with the wealth of digital technologies and toolsthat have spread over the past decades. Today’s society has changed and even the schoolmust be able to exploit all resources that social networks make available for capturing theattention of “digital natives”.Methods. The student becomes an active constructor of knowledge, integrating it in apersonal way. We talk about “didactics 2.0”, i.e. the study and improvement of teachingactivity achievable with the use of ICT tools. To create a learning environment, we musttake into account the collaboration of several parties (teachers, experts in the fi eld, students,parents), not forgetting the complexity and multiplicity of the surrounding reality. Thislearning environment can profi tably use social networks; students show to learn better ifthey fi nd meaningful the subjects they are studying.Results. The paper illustrates tools and forms of collaboration achievable with thedigital educational school environment. Social networks are used by students to thepoint of becoming a real language for “digital natives”. When we talk about digitalculture, we want to highlight the participation and digitalisation of traditional culturalprocesses, from which the educational environment does not escape. In the “school2.0” approach there are some peculiarities leading the school system towards the newgenerations.Conclusions. The need to “digitise” the school is a current hot topic, also if in everydayreality the work to do is still a lot: non-existent or obsolete computer labs, teachers who donot consider computers as a resource. Digitised teaching can be realised according to multiplechannels that have different characteristics and purposes.
Breast Cancer Prediction Using Neuro-Fuzzy Systems

Cancer is one of the most dangerous diseases in the world. The scientists are in pursue of finding better methods of detecting the various type of cancerous cell formations in the tissues. The purpose of this work is to develop a more accurate prediction model to identify breast cancer. In this work, Genetic algorithm (GA) based trained recurrent fuzzy neural network (RFNN) and adaptive neuro-fuzzy inference system (ANFIS) are used on the dataset provided by the UCI Machine Learning Repository. In this data set there are 9 quantitative attributes and a label that clinical features are observed or measured for 116 participants. The dataset separated into two sub-sets; one for training (81 instances) and one for testing (35 instances). For 8 different combinations of variables 8 different GA based trained RFNN and 8 different ANFIS were designed. The sensitivity, specificity, precision, F-score, probability of the misclassification error (PME) and accuracy of the training set, testing set and overall performances of the models were analyzed. The RFNN with 9 variables gave the highest overall accuracy (88.79%). The overall results showed that the GA based trained RFNN outperformed both ANFIS and other previous works that used the same dataset.
Multi-Agent Reinforcement Learning with Multi-Step Generative Models

We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace. For non-trivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed this problem by learning latent variable models for trajectory segments and optimizing over behavior in the latent space. In this work, we investigate whether this approach can be extended to 2-agent competitive and cooperative settings. The fundamental challenge is how to learn models that capture interactions between agents, yet are disentangled to allow for optimization of each agent behavior separately. We propose such models based on a disentangled variational auto-encoder, and demonstrate our approach on a simulated 2-robot manipulation task, where one robot can either help or distract the other. We show that our approach has better sample efficiency than a strong model-free RL baseline, and can learn both cooperative and adversarial behavior from the same data.
Unitas Multiplex. Biological architectures of consciousness

The so-called Posthuman question -the birth of organisms generated by the encounter of biological and artificial entities (humanoid robots, cyborgs and so on)is now on the agenda of science and, more generally, of contemporary society.This is an issue of enormous importance, which not only poses ethical questions but also, and above all, methodological questions about how it will be achieved on a scientific plane.How such entities will be born and what their functions will be?For example, what kind of consciousness will they be equipped with, in view of the function of consciousness for distinguishing the Self from others, which is the foundation of the interactive life of relationships?Many scholars believe that rapid technological progress will lead to the emergence of organisms that will simulate the functions of the mind, learn from their experiences, decode real-world information, and plan their actions and choices based on their own values elaborated from vast amounts of data and metadata.In the not-too-distant future, it is believed that these entities will acquire awareness and, consequently, decisional freedom, and perhaps even their own unique morals.In this paper, we try to show that the path towards this goal cannot avoid clarification of the problems that neuroscience has ahead of it.These problems concern: a) the way in which consciousness comes about on the basis of well-defined brain processes; b) how it represents its own organization and not a simple brain function; c) how simultaneously contains multiple distinct contents, each with its own intentionality; d) how it expresses dynamic evolutionary relations and not a set of phenomena that may be isolated; e) finally, how its order is not rigidly hierarchical, but is supported by a multiplicity of horizontal levels, each of which is in structural and functional continuum with different phenomenal events.The empirical and theoretical research effort on this topic provides an intensive contribution to the development of IC Technologies.
Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews

Detecting the main aspects of a particular product from a collection of review documents is so challenging in real applications. To address this problem, we focus on utilizing existing topic models that can briefly summarize large text documents. Unlike existing approaches that are limited because of modifying any topic model or using seed opinion words as prior knowledge, we propose a novel approach of (1) identifying starting points for learning, (2) cleaning dirty topic results through word embedding and unsupervised clustering, and (3) automatically generating right aspects using topic and head word embedding. Experimental results show that the proposed methods create more clean topics, improving about 25% of Rouge–1, compared to the baseline method. In addition, through the proposed three methods, the main aspects suitable for given data are detected automatically.
CNN Denoisers as Non-Local Filters: The Neural Tangent Denoiser.

Convolutional Neural Networks (CNNs) are now a well-established tool for solving computational imaging problems. Modern CNN-based algorithms obtain state-of-the-art performance in diverse image restoration problems. Furthermore, it has been recently shown that, despite being highly overparameterized, networks trained with a single corrupted image can still perform as well as fully trained networks, a phenomenon encapsulated in the deep image prior. We introduce a novel interpretation of denoising networks with no clean training data in the context of the neural tangent kernel (NTK), elucidating the strong links with well-known non-local filtering techniques, such as non-local means or BM3D. The filtering function associated with a given network architecture can be obtained in closed form without need to train the network, being fully characterized by the random initialization of the network weights. While the NTK theory accurately predicts the filter associated with networks trained using standard gradient descent, our analysis shows that it falls short to explain the behaviour of networks trained using the popular Adam optimizer. The latter achieves a larger change of weights in hidden layers, adapting the non-local filtering function during training. We evaluate our findings via extensive image denoising experiments.
Open source column

Following the last column on MatConvNet, let us continue to look at open source frameworks for deep learning. In this column we are going to check Keras, a Python API that allows to use several different backends like Tensorflow and CNTK. Actually, it also supports Theano, although the development of this framework has been halted by the original developers in 2017.
A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler

In various applications of satellite navigation and positioning, it is a key topic to select suitable satellites for positioning solutions to reduce the computational burden of the receiver in satellite selection system. Moreover, in order to reduce the processing burden of receivers, the satellite selection algorithm based on Gibbs sampler is proposed. First, the visible satellites are randomly sampled and divided into a group. The group is regarded as an initial combination selection scheme. Then, the geometric dilution of precision is chosen as an objective function to evaluate the scheme’s quality. In addition, the scheme is updated by the conditional probability distribution model of the Gibbs sampler algorithm, and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite. Furthermore, an “adaptive perturbation” strategy is introduced to improve the global searching ability of the algorithm. Finally, the extensive experimental results demonstrate that when the number of selected satellite is more than 6, the time that the proposed algorithm with the improvement of “adaptive perturbation” takes to select satellite once is 43.7% of the time that the primitive Gibbs sampler algorithm takes. And its solutions are always 0.1 smaller than the related algorithms in geometric dilution of precision value. Therefore, the proposed algorithm can be considered as a promising candidate for satellite navigation application systems.
Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Trace Link Recovery tries to identify and link related existing requirements with each other to support further engineering tasks. Existing approaches are mainly based on algebraic Information Retrieval or machine-learning. Machinelearning approaches usually demand reasonably large and labeled datasets to train. Algebraic Information Retrieval approaches like distance between tf-idf scores also work on smaller datasets without training but are limited in providing explanations for trace links. In this work, we present a Trace Link Recovery approach that is based on an explicit representation of the content of requirements as a semantic relation graph and uses Spreading Activation to answer trace queries over this graph. Our approach is fully automated including an NLP pipeline to transform unrestricted natural language requirements into a graph. We evaluate our approach on five common datasets. Depending on the selected configuration, the predictive power strongly varies. With the best tested configuration, the approach achieves a mean average precision of 40% and a Lag of 50%. Even though the predictive power of our approach does not outperform state-of-the-art approaches, we think that an explicit knowledge representation is an interesting artifact to explore in Trace Link Recovery approaches to generate explanations and refine results.
The Teaching Dimension of Q-learning

In this paper, we initiate the study of sample complexity of teaching, termed as (TDim) in the literature, for Q-learning. While the teaching dimension of supervised learning has been studied extensively, these results do not extend to reinforcement learning due to the temporal constraints posed by the underlying Markov Decision Process environment. We characterize the TDim of Q-learning under different teachers with varying control over the environment, and present matching optimal teaching algorithms. Our TDim results provide the minimum number of samples needed for reinforcement learning, thus complementing standard PAC-style RL sample complexity analysis. Our teaching algorithms have the potential to speed up RL agent learning in applications where a helpful teacher is available.
High-Resolution Crowd Density Maps Generation With Multi-Scale Fusion Conditional GAN

The major challenges for density maps estimation and accurate counting stem from the large-scale variations, serious occlusions, and perspective distortions. Existing methods generally suffer from the blurred density maps, which are caused by average convolution kernel, and the ineffective estimation across different crowd scenes. In this paper, we propose a multi-scale fusion conditional generative adversarial network (MFC-GAN) that can generate high-resolution and high-quality density maps. The fusion module of MFC-GAN is embedded in a multi-scale generator and discriminator architecture with a novel adversarial loss, which is designed to guide high-resolution density maps generation. In order to address the problem of scale variation, we further propose a bidirectional fusion module. It combines deep global semantic features and shallow local information by leveraging feature maps presented in different layers of the generator. Furthermore, in order to increase the effectiveness of the multi-scale fusion, we design a cross-attention fusion module, which weights the multi-scale fused feature and learns context-aware feature maps for generating high quality density maps. The experiments on four challenging datasets show the effectiveness, feasibility and robustness of the proposed MFC-GAN.
Study on the 2D Optimization Simulation of Complex Five-Hole Cutting Blasting under Different Lateral Pressure Coefficients

With the decrease and depletion of shallow coal resources, the depth of mining is increasing. The mechanism of high crustal stress blasting is not clear, and the effect of crustal stress on blasting effect is obvious. The law of the differential detonation is similar without crustal stress. The crustal stress of rock masses increases linearly with the increase in excavation depth, and the influence of crustal stress on blasting effect is great. In order to study the rock-breaking process of complex differential blasting under deep high crustal stress, the instantaneous detonation of each model and the priority detonation of the central hole are numerically simulated. The evolution law of the blasting crack and the method of optimizing blasting effect by differential blasting and increasing the aperture of the priority detonation hole under high crustal stress are put forward. The authors proposed a study on the 2D optimization simulation of complex five-hole cutting blasting under different lateral pressure coefficients of 400 m and 800 m with software ANSYS/LS-DYNA and analyzed the evolution rules of blasting cracks and lateral pressure coefficients. The results show that setting delayed detonation and hole diameter and optimizing blasthole spacing can optimize the rock-breaking effect under high crustal stress and different lateral pressure coefficients.
ADAPTATION OF LEAN PRODUCTION TOOLS TO EDUCATIONAL ACTIVITIES OF UNIVERSITIES

The use of lean technologies can improve the quality of services and the efficiency of available resource spending, stimulate the development of the country economy and improve the living standards of the population. Authorities pay particular attention to the modernization of higher education. In this regard, it is advisable to introduce lean technologies in the educational process of higher educational institutions, which will allow us to identify and develop the ability of these structures to self-development not only due to the influx of information and external finances, but through the use of their internal sources. The introduction of lean production principles in the activities of universities can help increase the efficiency of the educational process and their competitiveness. Based on this, the article discusses the process of lean manufacturing tool introduction into educational activities. An attempt is made to analyze comparatively lean manufacturing tools and the possibility of their application in the educational activities of the university. The article emphasizes that the replication of existing experience of lean manufacturing tool implementation will ensure a steady growth of key performance indicators and will allow the formation of a sustainable lean model of population behavior based on the popularization of the culture of “thrift” among students.
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support

Universal probabilistic programming systems (PPSs) provide a powerful framework for specifying rich probabilistic models. They further attempt to automate the process of drawing inferences from these models, but doing this successfully is severely hampered by the wide range of non--standard models they can express. As a result, although one can specify complex models in a universal PPS, the provided inference engines often fall far short of what is required. In particular, we show that they produce surprisingly unsatisfactory performance for models where the support varies between executions, often doing no better than importance sampling from the prior. To address this, we introduce a new inference framework: Divide, Conquer, and Combine, which remains efficient for such models, and show how it can be implemented as an automated and generic PPS inference engine. We empirically demonstrate substantial performance improvements over existing approaches on three examples.
Alleviating Privacy Attacks via Causal Learning

Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model’s predictions are used on an unseen data distribution. To alleviate privacy attacks, we demonstrate the benefit of predictive models that are based on the causal relationships between input features and the outcome. We first show that models learnt using causal structure generalize better to unseen data, especially on data from different distributions than the train distribution. Based on this generalization property, we establish a theoretical link between causality and privacy: compared to associational models, causal models provide stronger differential privacy guarantees and are more robust to membership inference attacks. Experiments on simulated Bayesian networks and the colored-MNIST dataset show that associational models exhibit upto 80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess.
Feature Analysis and Detection Techniques for Piracy Sites

In recent years, digital content has become easily accessible because of internet technology.Representative examples of such digital content include various types, such as music, TV, (program, sport, drama etc.) and films.However, there are cases where internet technology is used in illegal ways without the authorization of the copyright holder for digital content.Such actions have a direct impact on copyright owners' profits and further affect the development of the digital culture industry adversely.Therefore, in this study, we analyze features to detect piracy sites that cause copyright infringement.Further, we develop a piracy site detection crawler based on these features and present the analysis of its performance.
Research and Application of Data Exchange based on JSON

JSON is a lightweight data interchange technology. In order to solve the problems in the process of integrating data of various agricultural application systems, such as heterogeneity, redundancy and low degree of sharing, on Henan Rural Information Integrated Service Platform, a data exchange scheme based on JSON Technology is designed by JAVA programming language in this study, and which achieved data interchange between portal platform and other agricultural application systems. It is proved that this scheme has some advantages, such as fast parsing and low data redundancy, high exchange efficiency, and so on.
Fast generalization error bound of deep learning without scale invariance of activation functions

In the theoretical analysis of deep learning, discovering which features of deep learning lead to good performance is an important task. Using the framework for analyzing the generalization error developed by Suzuki (2018), we derive a fast learning rate for deep neural networks with general activation functions. According to Suzuki (2018), scale invariance of the activation functions is essential to derive tight error bounds. While the rectified linear unit (ReLU; Nair and Hinton, 2010) satisfies scale invariance, the other famous activation functions, such as the sigmoid, the hyperbolic tangent functions, and the exponential linear unit (ELU; Clevert et al., 2016), do not satisfy this condition. The existing analysis indicates the possibility that deep learning with non scale invariant activations may have a slower convergence rate of O(1∕n) whereas with scale invariant activation functions it can reach a faster rate. In this paper, without scale invariance of activation functions, we derive the tight generalization error bound which is essentially the same as that of Suzuki (2018). From this result, at least in the framework of Suzuki (2018), we show that scale invariance of the activation functions is not essential to obtain a fast rate of convergence. We also conclude that the theoretical framework proposed by Suzuki (2018) can be widely applied to the analysis of deep learning with general activation functions.
Implementasi QR Code untuk Efisiensi Waktu Pemesanan Menu Makanan dan Minuman di Restoran maupun Kafe

The quick response (QR) code provides a fast, easy, convenient, accurate and automatic method of transporting data. By freeing applications and popularizing wireless communications and cellular technology, two-dimensional barcode technology has been used for production, logistics, and sales. To order the benefits of technology to the service sector, for this study, we used various QR code applications in the food or beverage menu service management system in restaurants / cafes. The specific purpose is to allow restaurant waiters to quickly and appropriately provide service to restaurant / cafe customers. Experimental results show that the method developed in this study can significantly improve the service menu, prepare waiters and chefs to provide the right service, shorten the time for ordering the menu, and facilitate the resolution of the problem. Research in actual circumstances will be a full support tool for restaurant / cafe menu services. The results of the test prove agreeing from the point of view of efficiency and cost savings, the new system is fast and efficient, improving very competitive with the release of an improved management system.