Reviewerly/exHarmony huggingface dataset:
Statistical Local Binary Patterns (SLBP): Application To Prostate Cancer Gleason Score Prediction From whole Slide Pathology Images

Computerized whole slide image analysis is important for assisting pathologists in cancer grading and predicting patient clinical outcomes. However, it is challenging to analyze whole slide image (WSI) at cellular level due to its huge size and nuclear variations. For efficient WSI analysis, this paper presents a general texture descriptor, statistical local binary patterns (SLBP), which is applied to prostate cancer Gleason score prediction from WSI. Unlike traditional local binary patterns (LBP) and many its variants, the presented SLBP encodes local texture patterns via analyzing both median and standard deviation over a regional sampling scheme, so that it can capture more microand macro-structure information in the image. Experiments on Gleason score prediction have been performed on 317 different patient cases selected from the cancer genome atlas (TCGA) dataset. The presented SLBP descriptor provides over 80% accuracy on two-class (grade ≤7 vs grade ≥8) distinction, which is superior to traditional texture descriptors such as histogram, Haralick and other state-of-the-art LBP variants.
Neuro-Correlates of the eBICA Model

The article presents a new approach to building semantic maps based on the use of the eBICA model and the calculation of neurophysiological correlates of behavioral motives based on fMRI data. In this study, a social videogame paradigm was used in combination with fMRI and other recording tools. The obtained data show the complex neural network dynamics of behavioral motives distributed on the cortical and subcortical regions of the brain, related to the interaction of a person with the collaborative non-player character powered by the eBICA cognitive architecture.
Semi-supervised Detector Training with Prototypes for Vehicle Detection

Adapting detectors to new datasets is needed in scenarios where a user has a specific dataset that contains novel classes or is recorded in a setting where a pretrained detector fails. While detectors based on Convolutional Neural Networks (CNNs) are state-of-the-art and nowadays publicly available, they suffer from bad generalization capabilities when applied on datasets that notably differ from the one they were trained on. Finetuning the detector is only possible if the dataset is large enough to not destroy the underlying feature representation. We propose a method where only a few prototypes are labeled for training in a semi-supervised manner. In particular, we separate the detection from the classification step to avoid impairing the bounding box proposal generation. Our trained prototype classification network provides labels to automatically source a large dataset containing 20 to 30 times more samples without further supervision, which we then use to train a more powerful network. We evaluate our method on a private vehicle dataset with six classes and show that evaluating on a previously unseen recording site we can gain an accuracy increase of 9% at same precision and recall levels. We further show that finetuning with as few as 25 labeled samples per class doubles accuracy compared to directly using pretrained features for nearest neighbor classification.
Local and global elements of Spanish in Madrid

This chapter explores the term globality will be used to refer to the consequences of globalization, in the two senses explained by Blommaert, but with special interest in the consequences related to the Spanish language and its varieties and, secondarily, to other languages. It presents a general panorama of Madrid as a global city, giving special attention to the linguistic and communicative realities that characterize this urban space. The panorama begins with a description of trends in demography, economy, industry, communications technology, and culture that have given rise to Madrid’s status as global city. With the growing number of sociolinguistic studies, the 1990s gave rise to studies examining what were apparently peripheral features of Madrid speech. Madrid occupies a salient position in online communications, especially social media and social networking sites such as Twitter and Facebook.
AI-Based Technical Approach for Designing Mobile Decision Aids

Conversational Voice User Interfaces (VUIs) help us in performing tasks in a wide range of domains these days. While there have been several efforts around designing dialogue systems and conversation flows, little information is available about technical concepts to extract critical information for addressing the users’ needs. For conversational VUIs to function appropriately as a decision aid, artificial intelligence (AI) that recognizes and supports diverse user decision strategies is a critical need. Following the design principle proposed by Kwon et al. [1] regarding the conversational flow between the user and conversational VUI, we developed an AI-based mobile-decision-aid (MODA) that predictively models and addresses users’ decision strategies to facilitate users’ in-store shopping decision process. In this paper, technical details about how MODA processes users’ natural language queries and generate the most appropriate and intelligent recommendations have been discussed. This developmental approach provides broad implications to conversational VUIs for diverse complex decision-making contexts and decision-makers with a critical need for decision assistants.
Fiber-based Photonic-FPGA Architecture and In-Fiber Computing

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. In this presentation we will present a conceptual design for in-fiber optical neural network, i.e. a fiber-based realization of a photonic-FPGA. Neurons and synapses are realized in two ways: first as individual silica cores in a multi-core fiber and then within a multi-mode fiber. In the first realization optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. Simulations and experimental validation show classification and learning capabilities. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks. In the second type of realization we propose the design of an optical artificial neural network-based imaging system that has the ability to self-study image signals from an incoherent light source in different colors. Our design consists of a multi-mode fiber realizing a stochastic neural network. We show that the signals, transmitted through the multi-mode fiber, can be used for image identification purposes and can also be reconstructed using artificial neural networks with a low number of nodes.
Performance Evaluation of SVM and Neural Network Classification Methods for Diagnosis of Breast Cancer

Breast cancer is the major and detrimental ailment amid all of the afflictions. Females are regularly affected through this disease. Data mining is a knowledge innovation progression to detect the sickness among enormous quantity of information. We proposed an approach used for the prognostication of tumor and presented through support vector machine and neural network classification methods. 10-fold and 5-fold cross validations are applied in the intended system to obtain precise results. The breast cancer database is used in this procedure which is from UCI machine learning repository. By using WEKA tool we studied the both classification techniques which are support vector machine and neural network classification models with 5 and 10-fold cross validations. In addition, support vector machine with 5-fold cross validation got high accuracy.
Pengembangan Aplikasi Terintegrasi Sebagai Pusat Data Pengaduan Warga Untuk Layanan Smart City Di Kota Jambi

Jambi is one of 25 cities in Indonesia that are declared eligible to implement Smart City. This is as stated in the signing of the minutes by the Ministry of Communication and Information at the Movement Towards Smart City held in Cibubur in May last year. One of the efforts of the Jambi City Government is to provide digital literacy to the community, creating various types of media that can facilitate bureaucracy, provide space for the community to adapt to emerging technologies by collaborating with communities and academics. This also becomes a solution to the disparity between the community and the government in reporting various kinds of problems that exist in Jambi City. Not to mention the bureaucratic processes at the government level that are usually complex in solving these problems. This study aims to create a media that is able to provide freedom to citizens to share complaints through applications that are integrated directly with field technical implementers to leaders who have decision-making authority to accelerate implementation of the solution of incoming complaints.
Visualizing Parameter Spaces of Deep-Learning Machines

This paper applies visualization techniques to the high-dimensional parameter spaces of a particular type of neural network, the Multi-Layer Perceptron (MLPs). MLPs are the archetypal “deep-learning” machines, digital imitations of biological brains. MLP parameter spaces are data structures holding the neuron interconnect-weights “learned” by these machines during training. These structures are abstractions of biological systems and are not generally amenable to analysis by conventional means.
Cooperative Cheap Automated Guided Vehicles

The industrial environment in which independent platforms have to ensure the transfer of semi-finished products is dense in events. Elimination of collision's possibilities, blockages related to possible defects or supply problems would provide a significant increase in production line productivity. Cheap Automated Guided Vehicles (CAGV) will lead to even more agglomeration of work space, which requires collaborative communication between systems. This article addresses the implementation of a communication module between the experimental CAGV s that have been made so that collisions can be prevented, but also the transfer speed can be adapted to the change of workflow at master's request. At the end of the article are presented the possibilities of expanding the communication capabilities between CAGV at the level of an enterprise that falls within the concept of Industry 4.0.
MIXED REALITY ESCAPE ROOM VIDEO GAME TO PROMOTE LEARNING IN INDOOR ENVIRONMENTS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 5857-5864ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1412Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Amazon Reviews as Corpus for Sentiment Analysis Using Machine Learning

Most of the users today, provide their reviews on the various products on the Amazon website. The reviews provided by users are usually compact and demonstrative. For this reason, it becomes an affluent source for sentiment analysis. The objective of the paper is obtaining comparisons of the working of four standard Machine Learning algorithms for classifying the sentiments of the considered Amazon product reviews dataset. In our work, we determine the performance of these algorithms, that is, how accurately can they classify the sentiment of an unknown review. The paper provides a brief insight on sentiment analysis and the comparison of the performance of the considered algorithms of the classification of the sentiments based on several performance metrics.
PRACTICE-ORIENTED TRAINING AS A MECHANISM OF PROFESSIONAL EXPERIENCE DEVELOPMENT

1 Ural Federal University (RUSSIAN FEDERATION)2 Ural Federal University, Ural State Law University (RUSSIAN FEDERATION)
Framework-supported mechanism of testing algorithms for assessing memory and detecting disorientation from IoT sensors

Assessing memory in daily life can be useful for both early detection and tracking the symptoms of neurodegenerative disease such as Alzheimer's disease. One of these symptoms is disorientation, which can cause domestic accidents. With the avenue of Internet of Things (IoT), houses can collect big data in real-time for being processed and acquiring knowledge between both edge computing (local processing for avoiding communication overload) and the cloud computing conforming the fog computing. What sensors are needed and how to collect the information is something that requires actually preparing prototypes of this setup in a lab. As an alternative, this work proposes a cheaper method which is based on the use of realistic 3D simulations of the scenarios to consider. Using these scenarios, different algorithms for assessing memory can be compared and evaluate how well they detect disorientation of a person living in a house by means of tracking the movement of a person. The contribution of this paper is a case study where researchers can design the scenarios, including the activity of the daily living of the individuals, the array of sensors to be deployed, and the disorientation detection algorithms.
The Characteristics and Countermeasures of Direct Investment and Trade of South Korea in China

Purpose - This research is based on South Korea’s direct investment and trade status with China from 1992 to recent years, meaningful in establishing future strategies for China, which entered the New Normal era. Design/methodology/approach - This research used various economic statistics released in South Korea and China to identify the current situation and trends, and to present policy implications for South Korea’s direct investment in China and ways to expand trade. Findings - South Korea’s accumulated direct investment in China is continuing with steady investment, with the total accumulated amount of direct investment in China excluding tax havens and Hong Kong leading to its ranking in fourth place after Japan, Singapore and the U.S. in 2018. Meanwhile, the trade volume between South Korea and China at the time of the normalization of diplomatic ties has increased by 42 times to $268.6 billion as of 2018, with China being South Korea’s largest exporter since 2003 and Korea continuing its status as China’s largest importer since 2013. Research implications - The policy implications of this paper are thus: first, it is necessary to enhance efficiency in accordance with China’s push for “supply side reform”. Second, innovative manufacturing industries are urgently needed to establish industrial strategies in accordance with the implementation of new industrial policies, such as “Chinese Manufacturing 2025”, which is currently being promoted with the trend of the times. Third, it is necessary to find new models that conform to the ‘One-Belt and One-Road’ strategy and which expand cooperation using ‘AIIB’.
Time-Varying Interaction Estimation Using Ensemble Methods

Directed information (DI) is a useful tool to explore time-directed interactions in multivariate data. However, as originally formulated DI is not well suited to interactions that change over time. In previous work, adaptive directed information was introduced to accommodate non-stationarity, while still preserving the utility of DI to discover complex dependencies between entities. There are many design decisions and parameters that are crucial to the effectiveness of ADI. Here, we apply ideas from ensemble learning in order to alleviate this issue, allowing for a more robust estimator for exploratory data analysis. We apply these techniques to interaction estimation in a crowded scene, utilizing the Stanford drone dataset as an example.
End-to-End Probabilistic Inference for Nonstationary Audio Analysis

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis. We show how time-frequency analysis and nonnegative matrix factorisation can be jointly formulated as a spectral mixture Gaussian process model with nonstationary priors over the amplitude variance parameters. Further, we formulate this nonlinear model's state space representation, making it amenable to infinite-horizon Gaussian process regression with approximate inference via expectation propagation, which scales linearly in the number of time steps and quadratically in the state dimensionality. By doing so, we are able to process audio signals with hundreds of thousands of data points. We demonstrate, on various tasks with empirical data, how this inference scheme outperforms more standard techniques that rely on extended Kalman filtering.
RankNet for evaluation functions of the game of Go

In this paper, we present a new algorithm for learning evaluation functions of the game of Go. Recently AlphaGo Zero and AlphaZero have shown that accurate evaluation functions can be constructed by using deep neural networks. Such a training, however, requires an enormous amount of computational r esources that are not available for most researchers. One of the next challenges in this domain is constructing accurate evaluation functions with lesser computational resources. To tackle this problem, we apply the RankNet algorithm to training an AlphaGo Zero style unified Policy and Value network in a learning-to-rank fashion. Using the pairwise RankNet training increases the potential number of training examples and alleviates the requirements for the number of game records. Our modified RankNet algorithm trains both value and policy losses and its joint training makes the learning stable. Experimental results showed that neural networks trained by our algorithm showed higher playing strength than other methods, especially when the dataset sizes were relatively limited.
Three-dimensional range geometry compression via reduced entropy encoding of the image

The embedding approach of three-dimensional range geometry in two-dimensional images has led to a significant increase in the 3D-data compression ratio with respect to the image compression techniques development. With this approach, the main issue changed to maximum capacity utilization in the images, such as the number of color channels and bit depth. This was attained by reducing the number of interference patterns used as well as exploiting more powerful compression algorithms, such as free lossless image format. But this paper points out that compared to using conventional compression algorithms, modifying the mentioned process (two-dimensional image creation and compression) and finding the structure of the image data and its corresponding redundancy is preferable. Due to this effort, we will see a significant reduction in entropy of the information contained in the image, and this will create a new opportunity for improvement in lossless compression. Experimental evaluation and results verify the overall robustness of the proposed method. For example, a significant 0.29 bit per point ratio can be achieved for an object with a continuous smooth surface.
Reducing Kernel Surface Areas for Isolation and Scalability

Isolation is a desirable property for applications executing in multi-tenant computing systems. On the performance side, hardware resource isolation via partitioning mechanisms is commonly applied to achieve QoS, a necessary property for many noise-sensitive parallel workloads. Conversely, on the software side, partitioning is used, usually in the form of virtual machines, to provide secure environments with smaller attack surfaces than those present in shared software stacks.
ABNOTIVITY AS A CHARACTERISTIC OF PROFESSIONAL THINKING OF A TEACHER

The article deals with psychological preparedness of teachers to work with gifted school students as an integral characteristic which is a combination of functional, operational and personality components. Teachers who successfully work with gifted school students are characterized by relationship between the structures of professionally important qualities (PIQ) and professionally important skills (PIS) and these structures complement each other. If teachers do not have high achievements in working with gifted school students, PIQ and PIS groups function separately, as two independent substructures. Comparative analysis of groups of teachers before and after training proved that their PIQ level has risen and the structure of their psychological preparedness has become plastic and flexible, thus enabling teachers to acquire new constructive kinds of activities leading to bigger success in working with gifted school students. None of the teachers working efficiently after training had low level of abnotivity...
Merge-and-Shrink Task Reformulation for Classical Planning

The performance of domain-independent planning systems heavily depends on how the planning task has been modeled. This makes task reformulation an important tool to get rid of unnecessary complexity and increase the robustness of planners with respect to the model chosen by the user. In this paper, we represent tasks as factored transition systems (FTS), and use the merge-and-shrink (M&amp;S) framework for task reformulation for optimal and satisficing planning. We prove that the flexibility of the underlying representation makes the M&amp;S reformulation methods more powerful than the counterparts based on the more popular finite-domain representation. We adapt delete-relaxation and M&amp;S heuristics to work on the FTS representation and evaluate the impact of our reformulation.
Financial Development, Exchange Rate Fluctuations and Debt Dollarization

This paper examines how financial development influences the debt dollarization of nonfinancial firms in a sample of emerging market economies (EMEs). The macroeconomic channels are identified from an optimal portfolio allocation model and assessed empirically using the accounting information of nonfinancial firms from 21 EMEs during 2009–2017. The results show that financial development, measured by the private credit-to-GDP ratio, mainly reduces the influence of exchange rate volatility in determining a firm's debt currency composition, among other channels. Furthermore, the effect of exchange rate volatility becomes statistically insignificant beyond an estimated threshold credit-to-GDP ratio of 100 percent.
Quality-guided image classification toward information management applications

Image information management (IIM) is a key technique to improve the performance of large-scale image retrieval. However, IIM is still a big challenge due to the large sum of image datasets and traditional algorithms cannot cope with this problem. In order to solve these disadvantages, we propose a novel image classification algorithm based on image quality assessment (IQA) for image information management. Specifically, we first incorporate both low-level, high-level features as well as quality scores for image representation, where we leverage convolution neural network for deep feature extraction. Then, deep feature vector can be generated by column-wise stacking. Thus, each image can be represented by a feature vector. We leverage GMM to learn the distribution of obtained feature vectors. Similar image categories have similar probability distributions, we leverage the learned GMM model to calculate the posterior probability and image can be classified into corresponding category. Experimental results demonstrate the performance of our proposed method, and image information management is easier to implement.