Reviewerly/exHarmony huggingface dataset:
Towards Unifying Neural Architecture Space Exploration and Generalization

In this paper, we address two fundamental questions in neural architecture design research: (i) How does an architecture topology impact the gradient flow during training? (ii) Can certain topological characteristics of deep networks indicate a priori (i.e., without training) which models, with a different number of parameters/FLOPS/layers, achieve a similar accuracy? To this end, we formulate the problem of deep learning architecture design from a network science perspective and introduce a new metric called NN-Mass to quantify how effectively information flows through a given architecture. We demonstrate that our proposed NN-Mass is more effective than the number of parameters to characterize the gradient flow properties, and to identify models with similar accuracy, despite having significantly different size/compute requirements. Detailed experiments on both synthetic and real datasets (e.g., MNIST and CIFAR-10/100) provide extensive empirical evidence for our insights. Finally, we exploit our new metric to design efficient architectures directly, and achieve up to 3x fewer parameters and FLOPS, while losing minimal accuracy (96.82% vs. 97%) over large CNNs on CIFAR-10.
Health Creation Training Of Bachelors Of Education In Informational Society

To establish the content of health-creation training of bachelors of pedagogical education in the informational society the authors of the paper carried out the configuration of knowledge and methods accumulated and used in various social - human and natural sciences. It is argued that individual and social health is being under serious threat in the informational society. On the one hand, in this society there is a danger of physical and intellectual degradation of the person (in connection with a sedentary lifestyle, the fact that many mental operations were delegated to machines, etc.). And, on the other hand, a person, his or her basic needs being satisfied, is tempted to "kill time", to have a questionable lifestyle (in terms of physical, mental and moral health). The authors believe that the health-creation training of teachers at the undergraduate level should be structured as an element of a systematic response to the challenges of the informational society. That is, the competence of its subject in the field of health-creation activities should be "in-built" into the system of competencies that allow the education to really become the key to the welfare of the individual. Based on this, the supra-professional (universal) health-creation competence should consist in the ability to strengthen and increase the natural (genetically transmitted) potential of a person and turn it into a basis for achieving professional and social success, personal self-development.
Assessment and validation of global horizontal radiation: a case study in Mauritius

Validated solar maps are very crucial for a holistic site selection, design and financial evaluation of photovoltaic (PV) projects. During this research, the temporal and spatial variations of global horizontal solar radiation are investigated. The performance of PV systems has been analyzed. A solar map has been developed and validated against performance of PV systems for Mauritius. For the purpose, initially global horizontal irradiance was measured at intervals of 30 s across five sites in the country since 2013. Measurement data showed that yearly averaged daily insolation varied between 3.25 kWh/m2 and 5.04 kWh/m2 across the country. Angstrom Prescott (AP) coefficients were then calibrated in order to model daily radiation at the measurement sites. AP coefficient, a, varied between 0.132 and 0.278 while, a + b varied between 0.662 and 0.831. With the aid of sunshine hour data obtained from Mauritius Meteorological Services, monthly averaged and yearly averaged insolation are computed for additional six sites in order to have a wide coverage of the country. Statistical performance indicators were computed and results showed good agreement between measurement and the linear univariate AP model. Yearly averaged daily global horizontal insolation for eleven sites were used to generate a solar map. The average annual insolation levels have been estimated to vary between 1237 kWh/m2 and 1901 kWh/m2 over the island. The latter values represent the average capacity utilization factor of photovoltaic plants, which are estimated to vary between 14.1% and 21.7%. The results add up to highly valuable information on solar exposure for adoption of off-grid and grid tied photovoltaic technologies.
A New Temporal Recommendation System based on Users’ Similarity Prediction

Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar users to provide suggestions for a target user. Users’ preferences often vary over time and many traditional collaborative filtering algorithms fail to consider this important issue. In this paper, a novel recommendation method is proposed based on predicting similarity between users in the future and forecasting their similarity trends over time. The proposed method uses the sequence of users’ ratings to predict the similarities between users in the future and use the predicted similarities instead of the original ones to detect users’ neighbours. Experimental results on benchmark datasets show that the proposed method significantly outperforms classical and state-of-the-art recommendation methods.
BlUrM(or)e : Revisiting gender obfuscation in the user-item matrix

Past research has demonstrated that removing implicit gender information from the user-item matrix does not result in substantial performance losses. Such results point towards promising solutions for protecting users’ privacy without compromising prediction performance, which are of particular interest in multistakeholder environments. Here, we investigate BlurMe, a gender obfuscation technique that has been shown to block classifiers from inferring binary gender from users’ profiles. We first point out a serious shortcoming of BlurMe: Simple data visualizations can reveal that BlurMe has been applied to a data set, including which items have been impacted. We then propose an extension to BlurMe, called BlurM(or)e, that addresses this issue. We reproduce the original BlurMe experiments with the MovieLens data set, and point out the relative advantages of BlurM(or)e.
Forecasting Model of Gross Regional Domestic Product (GRDP) Using Backpropagation of Levenberg-Marguardt Method

The GRDP is an important indicator to measure the economic growth of a region so that the GRDP forecast future needs. This paper intends to choose a better method for forecasting the GRDP of Bandung Regency. The method used in this study is the Autoregressive Integrated Moving Average (ARIMA) time series model and the Backpropagation of Levenberg-Marquardt or Feed Forward Neural Network (FFNN) method. To measure the accuracy of forecasting carried out using Mean Absolute Percentage Error (MAPE). The GRDP data obtained from the Bandung Regency Central Bureau of Statistics, in the years 2010-2016. The results of the analysis using the ARIMA model, obtained ARIMA (0, 1, 1) model, with MAPE of 3.90%. Meanwhile, analysis using Backpropagation of Levenberg-Marquardt obtained FFNN (1, 5, 1) model, with MAPE of 3.88%. Because the MAPE value in the FFNN (1, 5, 1) model is smaller than the MAPE value in the ARIMA (0, 1, 1) model, it can conclude that the Backpropagation of Levenberg-Marquardt method better used in forecasting the GRDP of Bandung Regency.
A two-layer optimization and control strategy for zinc hydrometallurgy process based on RBF neural network soft-sensor

Zinc hydrometallurgy technique is an important method to produce zinc metal. The control of the zinc hydrometallurgy process is related to the quality of the zinc product. Due to the presence of the unmodeled dynamics, low- frequency disturbances, and high-frequency disturbances, it is a challenge to maintain the production indices within the desired ranges while minimizing the process cost. In this study, a two-layer optimization and control strategy is proposed for the zinc hydrometallurgy process. In the upper layer, an optimal setting module is established to determine the set-points of production indices. A radial basis function (RBF) neural network is constructed to estimate the production indices on-line. An optimal control scheme with fuzzy-logic-based compensator is developed in the lower layer. The proposed strategy is applied to the iron removal process of zinc hydrometallurgy process. Experiments show the effectiveness of our proposed strategy.
Video Human Behaviour Recognition Based on Improved SVM_KNN for Traceability of Planting Industry

Multivariate data acquisition is a difficult problem in traceability of planting industry. Video-based human behaviour recognition technology can automatically identify various human behaviors in the process of crops planting, and realize automatic data collection. A feature extraction method based on three-dimensional skeleton of human body and an improved SVM_KNN method has been proposed in this paper to classify human behavior and realize multi-target human behavior recognition based on video. The experiment results show that the human behavior recognition method proposed in this paper can effectively identify different human behaviors in crop planting.
Monolith: An OBDM and knowledge graph management platform

In this demo we present MONOLITH, a novel application that combines Ontology-based Data Management (OBDM) capabilities with a Knowledge Graph platform. The idea of the system is to provide an integrated environment offering OBDM services such as ontology inspection, query answering, data quality checking, and to couple these services with a Knowledge Graph IDE. Users can access and explore enterprise data through the ontology and build Knowledge Graphs from them, by exploiting the underlying OBDM system. MONOLITH allows to query the Knowledge Graphs through a SPARQL endpoint, to integrate external resources such as Linked Open Datasets, and export the Knowledge Graphs for third-party analytics.
Spatio-temporal Active Learning for Visual Tracking

The success of state-of-the-art deep learning based trackers is fuelled by the large-scale datasets. However, not all training data has a gain on model performance, and some data can even degrade the performance of the model. Therefore, we aim to train state-of-the-art trackers using a part of labeled frame with high information and less data. To this end, we propose a novel framework called STAL(spatio-temporal active learning strategy), which is integrated with an efficient deep tracker and a spatio-temporal active learning strategy. Specifically, we first mine the most informative frames to boost the deep tracker based on the corresponding spatio-temporal response scores of the target. Then, the hard and easy frames are labeled from annotation and machine auto-annotation, respectively. Hard and easy sample pairs are generated from selected frames. To alleviate the impact of sample pairs with large loss, a self-paced fully convolutional Siamese network is proposed by introducing a norm negative regularization. The STAL framework will converge well and output promising tracking performance on several publicly available datasets.
A Sketch-Based System for Semantic Parsing

This paper presents our semantic parsing system for the evaluation task of open domain semantic parsing in NLPCC 2019. Many previous works formulate semantic parsing as a sequence-to-sequence(seq2seq) problem. Instead, we treat the task as a sketch-based problem in a coarse-to-fine(coarse2fine) fashion. The sketch is a high-level structure of the logical form exclusive of low-level details such as entities and predicates. In this way, we are able to optimize each part individually. Specifically, we decompose the process into three stages: the sketch classification determines the high-level structure while the entity labeling and the matching network fill in missing details. Moreover, we adopt the seq2seq method to evaluate logical form candidates from an overall perspective. The co-occurrence relationship between predicates and entities contribute to the reranking as well. Our submitted system achieves the exactly matching accuracy of 82.53% on full test set and 47.83% on hard test subset, which is the 3rd place in NLPCC 2019 Shared Task 2. After optimizations for parameters, network structure and sampling, the accuracy reaches 84.47% on full test set and 63.08% on hard test subset (Our code and data are available at https://github.com/zechagl/NLPCC2019-Semantic-Parsing ).
Blended Education: Patterns of Implementation at Sumy State University

Recent years have been marked by an increasing global trend in the implementation of blended learning techniques at tertiary level education. This tendency has been growing in Ukrainian universities. Although blended education in Ukraine seems nowadays to increase, the efficiency of using blended teaching is still unclear. Today most universities started using distance learning and transfer to the blended system of education in classes. The scholars argue about advantages and disadvantages of conventional and blended education as both methods have positive sides and drawbacks. The successful integration of blended education into the educational process was proved by numerous studies. The present study was designed to determine the effect of blended education on the learning process. The first question in the study was to identify the main spheres of blended education influencing the educational process. The second question sought to determine the readiness of Sumy State University students to transition to a blended educational process. At the first stage, a technical task was agreed, a program and tools were developed, a schedule of information gathering was developed. At the second stage, the collection of information, the collecting of questionnaires, their computer processing, the formed tables, and the reports were prepared. The responses obtained from the service of the students showed that most of the students clearly understand the system of blended learning, feel positive about it and are more motivated in their learning. This piloting experience launched the positive practice of implementation of blended education in the educational process, where it is highly recommended to use the elements of blended education in classroom activity as additional individual work in the virtual online environment as a widening effect of classroom work with the help of modern electronic devices and technologies.
A Colour Image Encryption Using Genetic Operators and Generalized Henon Chaotic Mapping

In this paper, a colour image encryption method is proposed on the basis of genetic operators and generalized Henon chaotic mapping. Firstly, the initial values of Henon chaotic mapping are generated based on the plain image. Then, genetic operators are applied for image encryption, which include three operations of selection, crossover, and mutation. The selection operation is performed on the bit-level, which can eliminate the strong correlations between the pixels. The cipher image is finally obtained after crossover and mutation operations. Experimental results show that the proposed method has a large key space, high information entropy, and strong robustness against chosen-plaintext attack, which can be applied for the secure applications.
Internet of Things (IoT)

The Internet of Things (IoT) is a novel paradigm that can be defined as a network of physical objects and everyday objects such as vehicles, devices, buildings and other items. This network is embedded with sensors, software, electronics, and network connectivity to enable these objects to collect and exchange data without requiring human to a computer or human to human interaction.
Machine Learning Based Interaction Technique Selection for 3D User Interfaces

A 3D user interface can be adapted in multiple ways according to each user’s needs, skills and preferences. Such adaptation can consist in changing the user interface layout or its interaction techniques. Personalization systems which are based on user models can automatically determine the configuration of a 3D user interface in order to fit a particular user. In this paper, we propose to explore the use of machine learning in order to propose a 3D selection interaction technique adapted to a target user. To do so, we built a dataset with 51 users on a simple selection application in which we recorded each user profile, his/her results to a 2D Fitts Law based pre-test and his/her preferences and performances on this application for three different interaction techniques. Our machine learning algorithm based on Support Vector Machines (SVMs) trained on this dataset proposes the most adapted interaction technique according to the user profile or his/her result to the 2D selection pre-test. Our results suggest the interest of our approach for personalizing a 3D user interface according to the target user but it would require a larger dataset in order to increase the confidence about the proposed adaptations.
Transfer Learning for Network Classification

In this paper, we will study the problem of node classification in networks with the use of transfer learning. In many real applications, it may often be difficult to find a sufficient number of labels on the nodes for the classification process. When typical social networks often have a significant amount of content in the form of text, it is much harder to characterize the nodes in terms of pre-defined properties of interest (or class labels). This is because the nodes in the social network are autonomously created by different entities, who may not label them in a way, which is specific or friendly to particular kinds of applications. The lack of availability of labels on the nodes is analogous to the training data paucity which is often encountered in the classification problem. However, there is often copious availability of text collections, which describe a wide variety of subjects, including the desired properties of interest. Since social network nodes are also often associated with some amount of text content, such collections can be used as a transfer bridge in the learning process. In this paper, we will examine the use of transfer learning from these domains in order to perform collective classification. We will present experimental results, which show the effectiveness of our approach.
A Surprising Density of Illusionable Natural Speech.

Recent work on adversarial examples has demonstrated that most natural inputs can be perturbed to fool even state-of-the-art machine learning systems. But does this happen for humans as well? In this work, we investigate: what fraction of natural instances of speech can be turned into illusions which either alter humans' perception or result in different people having significantly different perceptions? We first consider the McGurk effect, the phenomenon by which adding a carefully chosen video clip to the audio channel affects the viewer's perception of what is said (McGurk and MacDonald, 1976). We obtain empirical estimates that a significant fraction of both words and sentences occurring in natural speech have some susceptibility to this effect. We also learn models for predicting McGurk illusionability. Finally we demonstrate that the Yanny or Laurel auditory illusion (Pressnitzer et al., 2018) is not an isolated occurrence by generating several very different new instances. We believe that the surprising density of illusionable natural speech warrants further investigation, from the perspectives of both security and cognitive science. Supplementary videos are available at: this https URL.
Where the domination of maxitive kernels is extended to signed values

Convolution kernels are essential in signal processing. They are used to model sensors, to define filters, to ensure the interplay between continuous and discrete domains, etc. A classic shortcoming is the difficulty to define which kernel is suitable for a particular application. In previous articles, we relied on a simple analogy between positive convolution kernels and probability distributions to define the notion of maxitive kernel. A maxitive kernel aims at representing a convex set of positive convolution kernels. It therefore models imprecise information on the suitable kernel to be used. Though, in many applications, such as filtering, it may be necessary to use signed convolution kernels. In this article we propose to extend the notion of maxitive kernel domination over signed convolution kernels. This will lead us towards a concept little used until now that are signed – and thus non-monotonous – set functions.
Capturing Dialogue State Variable Dependencies with an Energy-based Neural Dialogue State Tracker

Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; (ii) applying an energy-based methodology to model dialogue state tracking as a structured prediction task; and (iii) evaluating the impact of inter-slot relationships on model performance. Overall we demonstrate that modelling the associations between target slots with an energy-based formalism improves dialogue state tracking performance in a number of ways.
Practical second Futamura projection: partial evaluation for high-performance language interpreters

Partial evaluation, based on the first Futamura projection, allows compiling language interpreters with given user programs to efficient target programs. GraalVM is an example system that implements this mechanism. It combines partial evaluation with profiling information and dynamic compilation, to transform interpreters into high-performance machine code at run time. However, partial evaluation is compile-time intensive, as it requires the abstract interpretation of interpreter implementations. Thus, optimizing partial evaluation is still subject to research to this day. We present an approach to speed up partial evaluation, by generating source code ahead of time, which performs partial evaluation specific to interpreter implementations. Generated code, when executed for a given user program at run time, directly emits partially evaluated interpreter instructions for language constructs it knows and sees in the program. This yields the target program faster than performing the first Futamura projection. The generated source code behaves similarly to a specialized partial evaluator deduced by performing the second Futamura projection, although no self-applying partial evaluator is involved during code generation.