Reviewerly/exHarmony huggingface dataset:
Learning Expensive Coordination: An Event-Based Deep RL Approach

Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly. However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league. Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination. The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses and ii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time. In this work, we address this problem through an event-based deep RL approach. Our main contributions are threefold. (1) We model the leader's decision-making process as a semi-Markov Decision Process and propose a novel multi-agent event-based policy gradient to learn the leader's long-term policy. (2) We exploit the leader-follower consistency scheme to design a follower-aware module and a follower-specific attention module to predict the followers' behaviors and make accurate response to their behaviors. (3) We propose an action abstraction-based policy gradient algorithm to reduce the followers' decision space and thus accelerate the training process of followers. Experiments in resource collections, navigation, and the predator-prey game reveal that our approach outperforms the state-of-the-art methods dramatically.
Learning robust word representation over a semantic manifold

The performance of the traditional Word2Vec model heavily depends on the quality and quantity of the corpus, which violates the way of the humans learn. To understand word meaning, human beings prefer a two-stage learning process. That is, reading a Linguist-compiled dictionary as well as doing reading comprehension. These two stages complement each other. Traditional Word2Vec is an analogy of reading comprehension. While the first stage, learning the semantic rules from a language dictionary, such as the knowledge of thesaurus and etymology, is usually ignored by existing methods. In this work, we propose a robust word embedding learning framework by imitating the two-stage human learning process. In particular, we construct a semantic manifold based on the thesaurus and etymology to approximate the first stage. Then, we regularize the second stage (Word2Vec model) with this semantic manifold. We train the proposed model on three corpora (Wikipedia, enwik9 and text8). The experimental results demonstrate that the proposed method learns much smoother vector representations. Also, the performance on learning word embedding is robust even when the method is trained with a very simple corpus.
Learning the Arrow of Time for Problems in Reinforcement Learning

We humans have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment. Drawing inspiration from that, we approach the problem of learning an arrow of time in a Markov (Decision) Process. We illustrate how a learned arrow of time can capture salient information about the environment, which in turn can be used to measure reachability, detect side-effects and to obtain an intrinsic reward signal. Finally, we propose a simple yet effective algorithm to parameterize the problem at hand and learn an arrow of time with a function approximator (here, a deep neural network). Our empirical results span a selection of discrete and continuous environments, and demonstrate for a class of stochastic processes that the learned arrow of time agrees reasonably well with a well known notion of an arrow of time due to Jordan, Kinderlehrer and Otto (1998).
A Bibliometric Analysis on Blockchain Technology Literature in Academic Journals

A blockchain is a revolutionary system that connects computer networks in a decentralized and distributed manner. Blockchain allows the process of current transactions to be peer-to-peer (P2P) without relying on a single server. Once entered, information can never be erased. The blockchain contains a certain and verifiable record of every single transaction ever made. This study evaluated the development of research publications in the implementation process and future trend of Blockchain Technology. The aim of this study is to assist researchers in creating a theoretical framework and to provide a preliminary source of references in research mapping in this field. All research publications related to the implementation and future trend of Blockchain Technology in the Scopus database were analyzed using several bibliometric indicators. The authorship, number of citations, journal sources, publishers, institutions, and countries, year of publication, categories, and author keywords were examined.
Penerapan Model Pembelajaran Kooperatif dengan Media Audio Visual Berbasis Teknologi Untuk Meningkatkan Aktivitas dan Kemampuan Menyimak Mahasiswa

This research aims to increase the activity and listening skill of students through a cooperative learning model of technology-based audio visual media at the course of language basic skills. The subjects were the students of PGSD in III B class academic year 2019/2020 which amounted to 40 students. The research instruments were observation sheets and test sheets of the listening skill. The observation data was analyzed by using the average score and the data of the listening capabilities analyzed by using of the average value and percentage. The results showed that (1) the implementation of cooperative learning models with technology-based visual audio media at the course of the language basic skills can increase the learning activity, (2) The application of cooperative learning model with technology-based audio visual media in the course of basic language skills can enhance the ability to listen of students who are infused with the ability to listen to the students each cycle increases. The average scor of the students ' listening skills at the first cycle is 78.05 with a detailed guidance of a kilacical study is 77.5% increased in the second cycle the average value of the student's listening capability is 83.25 with the classical guidance of the study is 92.5%. It is thus concluded that the application of cooperative learning models with technology-based audio visuals can increase the activity and listening skill of IIIB students at the language Basic skills Lecture at the PGSD University of Bengkulu.
Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems

Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such as classification and detection. However, BN layers introduce complexity and computational overheads that are highly undesirable for training and/or inference on low-power custom hardware implementations of real-time embedded vision systems such as UAVs, robots and Internet of Things (IoT) devices. They are also problematic when batch sizes need to be very small during training, and innovations such as residual connections introduced more recently than BN layers could potentially have lessened their impact. In this paper we aim to quantify the benefits BN layers offer in image classification networks, in comparison with alternative choices. In particular, we study networks that use shifted-ReLU layers instead of BN layers. We found, following experiments with wide residual networks applied to the ImageNet, CIFAR 10 and CIFAR 100 image classification datasets, that BN layers do not consistently offer a significant advantage. We found that the accuracy margin offered by BN layers depends on the data set, the network size, and the bit-depth of weights. We conclude that in situations where BN layers are undesirable due to speed, memory or complexity costs, that using shifted-ReLU layers instead should be considered; we found they can offer advantages in all these areas, and often do not impose a significant accuracy cost.
Evaluation and Selection of IoT Service for Smart House System Big Data Processing

In this paper, the functional features of IoT services, as well as the criteria for their evaluation and selection were investigated. Existing international standards of cloud services quality for IoT systems have been analysed. A recommendation technique has been developed for selecting an IoT service for storing, processing and visualizing data from the Smart House system. Practical examples of IoT services and cloud platforms usage for the remote laboratory Smart House & IoT are shown.
HOW BUSINESS SIMULATION METHODOLOGIES CAN IMPACT ON THE IMPROVEMENT OF STUDENTS’ SKILLS

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7327-7331ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1741Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Image Encryption System Based on a Nonlinear Joint Transform Correlator for the Simultaneous Authentication of Two Users

We propose a new encryption system based on a nonlinear joint transform correlator (JTC) using the information of two biometrics (one digital fingerprint for each user) as security keys of the encryption system. In order to perform the decryption and authentication in a proper way, it is necessary to have the two digital fingerprints from the respective users whose simultaneous authentication is pursued. The proposed security system is developed in the Fourier domain. The nonlinearity of the JTC along with the five security keys given by the three random phase masks and the two digital fingerprints of the two users allow an increase of the system security against brute force and plaintext attacks. The feasibility and validity of this proposal is demonstrated using digital fingerprints as biometrics in numerical experiments.
A Latent Morphology Model for Open-Vocabulary Neural Machine Translation

Translation into morphologically-rich languages challenges neural machine translation (NMT) models with extremely sparse vocabularies where atomic treatment of surface forms is unrealistic. This problem is typically addressed by either pre-processing words into subword units or performing translation directly at the level of characters. The former is based on word segmentation algorithms optimized using corpus-level statistics with no regard to the translation task. The latter learns directly from translation data but requires rather deep architectures. In this paper, we propose to translate words by modeling word formation through a hierarchical latent variable model which mimics the process of morphological inflection. Our model generates words one character at a time by composing two latent representations: a continuous one, aimed at capturing the lexical semantics, and a set of (approximately) discrete features, aimed at capturing the morphosyntactic function, which are shared among different surface forms. Our model achieves better accuracy in translation into three morphologically-rich languages than conventional open-vocabulary NMT methods, while also demonstrating a better generalization capacity under low to mid-resource settings.
Optimization of Costs for Maintenance of Backup Processes in the Conditions of Incomplete Information in Infocommunication Systems

The study of optimization problems of the components of a complex system can be carried out on the basis of a systems approach. In accordance with the principle of dual consideration, no system element is an isolated object, since it is included in another system of a higher level. In complex systems models, sufficiently integrable subsystems with given properties and relations between them should be considered as initial elements. Without taking into account the interconnectedness of the elements of the system, the results of local optimization may not only not correspond to the global extremum, but even contradict it. Therefore, in the systems approach, an important role is played by the study of the technological features of the system and its structure. The description of such components makes it possible to clarify the qualitative links that unite the individual links in the system, and also characterize them with the help of some quantitative relationships.
Gated Contiguous Memory U-Net for Single Image Dehazing

Single image dehazing is a challenging problem that aims to recover a high-quality haze-free image from a hazy image. In this paper, we propose an U-Net like deep network with contiguous memory residual blocks and gated fusion sub-network module to deal with the single image dehazing problem. The contiguous memory residual block is used to increase the flow of information by feature reusing and a gated fusion sub-network module is used to better combine the features of different levels. We evaluate our proposed method using two public image dehazing benchmarks. The experiments demonstrate that our network can achieve a state-of-the-art performance when compared with other popular methods.
Historization of Scientific Observation in Modern Scientific Researches

This article is devoted to historization of scientific practices as one of the central points in problem field of modern science studies. The subject of our article is scientific observation as one of the epistemic practices. Historization of scientific observation in modern scientific studies is possible, because of material practices and social relations begin to problematize in the scientific field. Science is no longer characterized only by a propositional order of representations. It is an assemblage of connections and relations between different agents and network of things, people and practices. This network is complexly arranged and branched, but in the same time it is coordinated in a certain optics and it is producing the visual closure to constructed object. This new optics, that makes visible the material and routine practices, puts in a new way the task to understand, how to work with heterogeneous and historically changeable field of practices and different “ways to do science”. There is a rethinking of the self-evident epistemic categories and particularly scientific observation. As an epistemic genre and scientific practice observation begins to take shape relatively late – only in the XVII century, when there is a complication and multiplication of practices of production of the visual images, that are making concrete from abstract and visible from invisible. To historicize scientific observation is to show how it has become a self-evident epistemic category and an integral scientific function. Scientific observation can be historicized as a set of practices that emerged and spread throughout a particular historical period, on the one hand, as practices of production, coordination, presentation and description of observational data. And on the other hand, it can be historicized as practices of production of “scientific self” as instances of observation. This article attempts to show that observation as a practice and as historically varied object of science is characterized, on the one hand, by the production of “that is visible” and, on the other hand, by “scientific self”.
Image correlation by one-dimensional signatures invariant to rotation, position, and scale using the radial Hilbert transform optimized

This paper presents a new methodology for pattern recognition invariant to rotation, position, and scale. The method uses the correlation of signatures, where the signatures were created with a new equation called the radial Hilbert transform optimized (RHTO) for longer signatures. An analysis with eight non-homogeneous illumination patterns was performed with 2000 letter variants and 30 phytoplankton species. The higher confidence level was founded using the radial Hilbert optimized methodology. Also, it utilized a correlation called adaptive linear–nonlinear correlation, which gave a better discrimination performance than the nonlinear correlation function.
Comparing a Hand-crafted to an Automatically Generated Feature Set for Deep Learning: Pairwise Translation Evaluation

The automatic evaluation of machine translation (MT) has proven to be a very significant research topic.Most automatic evaluation methods focus on the evaluation of the output of MT as they compute similarity scores that represent translation quality.This work targets on the performance of MT evaluation.We present a general scheme for learning to classify parallel translations, using linguistic information, of two MT model outputs and one human (reference) translation.We present three experiments to this scheme using neural networks (NN).One using string based hand-crafted features (Exp1), the second using automatically trained embeddings from the reference and the two MT outputs (one from a statistical machine translation (SMT) model and the other from a neural machine translation (NMT) model), which are learned using NN (Exp2), and the third experiment (Exp3) that combines information from the other two experiments.The languages involved are English (EN), Greek (GR) and Italian (IT) segments are educational in domain.The proposed languageindependent learning scheme which combines information from the two experiments (experiment 3) achieves higher classification accuracy compared with models using BLEU score information as well as other classification approaches, such as Random Forest (RF) and Support Vector Machine (SVM).
Robust Discrimination and Generation of Faces using Compact, Disentangled Embeddings

Current solutions to discriminative and generative tasks in computer vision exist separately and often lack interpretability and explainability. Using faces as our application domain, here we present an architecture that is based around two core ideas that address these issues: first, our framework learns an unsupervised, low-dimensional embedding of faces using an adversarial autoencoder that is able to synthesize high-quality face images. Second, a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors, each of which contains separated information about one of four major face attributes (pose, identity, expression, and style) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner. The resulting architecture achieves state-of-the-art image quality, good discrimination and face retrieval results on each of the four attributes, and supports various face editing tasks using a face representation of only 99 dimensions. Finally, we apply the architecture's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset.
Pose initialization method of mixed reality system for inspection using convolutional neural network

The Mixed Reality (MR) has become a trend in industrial applications such as inspection and maintenance thanks to the benefit of technological advances in computer vision. Simultaneous Localization And Mapping (SLAM) is a key component of the MR system which augments the CAD model of a target object in the live stream. However, the existing SLAM-based systems rely on a manual handling or a marker-based registration between the model coordinate and the global coordinate. In this paper, we present a non-marker based registration method which automatically performs both the target object detection in the live stream and its initial 3D pose estimation. We exploit two Convolutional Neural Networks (CNNs) to align the CAD model in a global map, and to detect the target object in keyframes of the SLAM system. Since manual preparation of training data is very laborious, we also propose a data argumentation method for the industrial application. The data augmentation method generates a synthesized dataset consisting of pairs of the RGB image and the corresponding camera pose using the object's CAD model. Two CNNs for the object detection in keyframes and the initial pose estimation are trained with the synthesized dataset, respectively. Our result shows that this method can robustly find the target object's initial pose without a dense point cloud or other features detected by hand-crafted descriptors.
Hierarchical segmentation from a non-increasing edge observation attribute

Hierarchical image segmentation provides region-oriented scale-spaces: sets of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph-based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. It computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should be merged according to the dissimilarity. We provide an explicit definition of the (edge-) observation attribute and Boolean criterion which are at the basis of this method and show that they are not increasing. Then, we propose an algorithm to compute all the scales for which the criterion holds true. Finally, we propose new methods to regularize the observation attribute and criterion and to set up the observation scale value of each edge of a graph, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy. Assessments on Pascal VOC 2010 and 2012 show that these strategies lead to better segmentation results than the ones obtained with the original HGB method.
Revisiting Probability Distribution Assumptions for Information Theoretic Feature Selection

Feature selection has been shown to be beneficial for many data mining and machine learning tasks, especially for big data analytics. Mutual Information (MI) is a well-known information-theoretic approach used to evaluate the relevance of feature subsets and class labels. However, estimating high-dimensional MI poses significant challenges. Consequently, a great deal of research has focused on using low-order MI approximations or computing a lower bound on MI called Variational Information (VI). These methods often require certain assumptions made on the probability distributions of features such that these distributions are realistic yet tractable to compute. In this paper, we reveal two sets of distribution assumptions underlying many MI and VI based methods: Feature Independence Distribution and Geometric Mean Distribution. We systematically analyze their strengths and weaknesses and propose a logical extension called Arithmetic Mean Distribution, which leads to an unbiased and normalised estimation of probability densities. We conduct detailed empirical studies across a suite of 29 real-world classification problems and illustrate improved prediction accuracy of our methods based on the identification of more informative features, thus providing support for our theoretical findings.