Reviewerly/exHarmony huggingface dataset:
Synchronizing hyperchaotic subsystems with a single variable: A reservoir computing approach

We adopt the machine learning technique known as “reservoir computing” to investigate hyperchaos synchronism. Interestingly, we find that by virtue of this approach, synchronization of hyperchaotic subsystems is available via only one dynamical variable. Specifically, we show that by sending just a single variable, synchronization of hyperchaotic subsystem and its learned reservoir computer can be achieved. In the same fashion, one can further observe synchronism in the trained reservoir computers even when their learned hyperchaotic subsystems are mismatched. Moreover, we demonstrate that the synchronization of interest is robust in the presence of relatively high levels of extensive noise. Our work provides an alternative way of realizing synchronization in hyperchaotic systems.
Efficient Teleportation for High-Dimensional Quantum Computing

Performance of an entangled quantum channel is affected by classical feedback assisted in quantum communications. For example, in quantum-gate teleportation schemes, the capacity of an independent entangled quantum channel is reduced by utilizing two-way simultaneous classical communication (TWSCC↔). However, by exploiting the superposition of high-dimensional quantum channels, the transmission efficiency of the quantum-gate teleportation can be dramatically improved with TWSCC↔. In this study, we investigate the possibility of achieving an efficient scheme of nonlocal high-dimensional quantum computation by using hyperentangled photon pairs, atoms, and an optical micro-resonator coupled system. The feasibility and efficiency of the scheme are also discussed. Results prove that, for nonlocal quantum computing, high-dimensional quantum operation performs better than traditional methods that decompose the high-dimensional Hilbert space into two-dimensional quantum space under limited prior shared maximally entangled resources.
Jumping Further: Forward Jumps in a Gravity-reduced Immersive Virtual Environment

In a cable-driven suspension system developed to simulate the reduced gravity of lunar or Martian surfaces, we propose to manipu-late/reduce the physical cues of forward jumps so as to overcome the limited workspace problem. The physical cues should be manipulated in a way that the discrepancy from the visual cues provided through the HMD is not noticeable by users. We identified the extent to which forward jumps can be manipulated naturally. We combined it with visual gains, which can scale visual cues without being noticed by users. The test results obtained in a prototype application show that we can use both trajectory manipulation and visual gains to overcome the spatial limit. We also investigated the user experiences when making significantly high and far jumps. The results will be helpful in designing astronaut-training systems and various VR entertainment content.
A Systemic Approach to City Image Building. The Case of Katowice City

Making long-term decisions and developing successful policy is always a challenge for a governing body. Within the framework of causal mapping, we evolved a model for the problem of creating and reinforcing a city’s image. Then, the model was enhanced into a quantitative form and processed with a novel approach: the extended form of the Weighted Influence Non-linear Gauge System (WINGS). A real-life case study of the city of Katowice showed that the presented approach can be helpful for city authorities. It reinforces the understanding of the problem, facilitates choosing policy options, and supports sustainable city development.
GSGP-C++ 2.0: A geometric semantic genetic programming framework

Geometric semantic operators (GSOs) for Genetic Programming have been widely investigated in recent years, producing competitive results with respect to standard syntax based operator as well as other well-known machine learning techniques. The usage of GSOs has been facilitated by a C++ framework that implements these operators in a very efficient manner. This work presents a description of the system and focuses on a recently implemented feature that allows the user to store the information related to the best individual and to evaluate new data in a time that is linear with respect to the number of generations used to find the optimal individual. The paper presents the main features of the system and provides a step by step guide for interested users or developers.
WEKIT.One: A Sensor-Based Augmented Reality System for Experience Capture and Re-enactment

Body-worn sensors can be used to capture, analyze, and replay human performance for training purposes. The key challenge to any such approach is to establish validity that the captured expert experience is actually suitable for training. In this paper, to evaluate this, we apply a questionnaire-based expert assessment and a complementary trainee knowledge assessment to study the approach adopted and the models generated with the WEKIT solution, a hardware and software application that complements Augmented Reality glasses with wearable sensor-actuator experience. This solution was developed using the ID4AR framework which as also developed within the WEKIT project. ID4AR framework is a domain agnostic framework which can be used to design augmented reality and sensor based applications for training. The study presented triangulates validity across three independent test-beds in the professional domains of aircraft maintenance, medical imaging, and astronaut training, with 61 experts completing the expert survey and 337 students completing the trainee knowledge test. Results show that the captured expert models were positively received in all three domains and the identified level of acceptance suggests that the solution is capable of capturing models for training purposes at large.
CNN-Based Phoneme Classifier from Vocal Tract MRI Learns Embedding Consistent with Articulatory Topology

Recent advances in real-time magnetic resonance imaging (rtMRI) of the vocal tract provides opportunities for studying human speech.This modality together with acquired speech may enable the mapping of articulatory configurations to acoustic features.In this study, we take the first step by training a deep learning model to classify 27 different phonemes from midsagittal MR images of the vocal tract.An American English database was used to train a convolutional neural network for classifying vowels (13 classes), consonants (14 classes) and all phonemes (27 classes) of 17 subjects.Classification top-1 accuracy of the test set for all phonemes was 57%.Error analysis showed voiced and unvoiced sounds often being confused.Moreover, we performed principal component analysis on the network's embedding and observed topological similarities between the network learned representation and the vowel diagram.Saliency maps gave insight into the anatomical regions most important for classification and show congruence with known regions of articulatory importance.We demonstrate the feasibility for deep learning to distinguish between phonemes from MRI. Network analysis can be used to improve understanding of normal articulation and speech and, in the future, impaired speech.This study brings us a step closer to the articulatory-to-acoustic mapping from rtMRI.
An Enlarging-the-Capacity Packet Sorting Covert Channel

The construction method of the covert channel has always been a topic of research and exploration in the field of information security, and the use of IP data packets for covert channel construction is also an important method.Based on the above content, this paper proposes an enlarging-the-capacity packet sorting covert channel.We establish enlarging-the-capacity packet sorting covert channel model and derive the functional relationship between the total number of covert information transmitted and the number of ports.This method can send more secret information when the network status is not ideal.The simulation results show that the covert channel of the extended packet sorting has high performance in different packet loss rates and delays, and is superior to other IP covert channel.
Multi-class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation

The Dice loss function is widely used in volumetric medical image segmentation for its robustness against the imbalance between the numbers of foreground and background voxels. However, it is not able to differentiate hard examples from easy ones, which usually comprise the majority of training examples and therefore dominate the loss function. In this work, we propose a novel loss function, termed as Gradient Harmonized Dice Loss, to both address the quantity imbalance between classes and focus on hard examples in training, with further generalization to multi-class segmentation. The proposed loss function is employed in a 3D fully convolutional neural network for multiple object segmentation of MRI knee joint images and validated on both public SKI10 dataset and 637 MRI knee scans collected from local hospitals. The experimental results show that the Gradient Harmonized Dice Loss outperforms the popular loss functions, such as Dice loss and Focal loss, and achieves the state-of-the-art results on the validation data of SKI10.
A Web Service Security Governance Approach Based on Dedicated Micro-services

Service-oriented architecture (SOA) is known to be characterized by its openness, its agility and its composability. Such architecture includes autonomous, interoperable and potentially reusable services, which are implemented as Web Services (WS). When deployed in the Cloud Environment, security threats may increase at the WS level, raising some security challenges, such as vulnerability discovery and trust or availability violation. Addressing security issues require complex tasks that should be treated separately with associated concerns. To deal with this problem, we refer here to an alternative architecture known as Micro-Services Architecture (MSA) which is based on the principle of decomposing large and complex software projects into many atomic sub-projects. In Addition, to control, manager and ensure that vulnerabilities are appropriately treated and the expected security objectives are achieved, we find that the governance of Web Service security is inevitably required. Hence, we suggest here a Web Service security governance approach aims to reduce the security flaws and to enhance trust between web services. This approach consists in combining micro-services by using a sub-set of GDPR(EU General Data Protection Regulation (GDPR) replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe, to protect and empower all EU citizens data privacy and to reshape the way organizations across the region approach data privacy.) rules and a set of defined security policies. This approach allows guessing the dedicated micro-service referring to user security requirements.
Alternative method for determining API degrees of petroleum in contaminated soil by FTIR

A method was developed to determine °API from the relative abundance of polar and aliphatic functional groups as measured by FTIR. Twenty-two samples of petroleum were collected from the southern region of Mexico and analyzed by FTIR using an Attenuated Total Reflectance (ATR) accessory. API gravity was run in parallel using the convention hydrometer method (ASTM D6822-12b). Variables were analyzed for principal components and a minimum of squares regression. API gravity of the petroleum used was between −8.8 and 41.5. For correlation, an index of the relative abundance of polar functional groups vs. saturates was developed. Data from calibration gave an R2 = 0.9632 and a standard error of 0.0129, with an absolute error of 0.0104. During the validation (with petroleum from contaminated sites), the results gave an R2 = 0.9024, with a standard error estimated at 0.0307 and an absolute mean error of 0.0199. Thus, the determination of API degrees of petroleum extracted from contaminated soil was shown to be viable using FTIR-ATR. The algorithm was developed to fit well for the whole range of crudes, from extra heavy (<10 °API) to super light (>39 °API) crudes. This method is simple, quick, does not require extensive sample preparation, uses very small sample proportions (<1 g) and produces only a minimum of waste as compared to the conventional (hydrometer) method.
Thorny problems in data (-intensive) science

Data scientists face challenges spanning academic and non-academic institutions.
On Classifiers for Feature-Based Automatic Modulation Recognition over D-STBC Cooperative Networks

This paper addresses the problem of semi-blind automatic digital modulation recognition (AMR) for wireless relaying network. The proposed recognition approach is based on a simplified Distributed Space-Time Block Coding (D-STBC) scheme for a three-node cooperative network system incorporating Amplify-and-Forward (AaF) relaying protocol network. The aim of this work is to recognize the modulation type and order among different M-ary shift keying linear modulations. In this paper, the Feature-Based Automatic Modulation Recognition (FB-AMR) is adopted through the comparison of the three most used machine learning classifiers , namely, support-vector machine (SVM), K-nearest neighbor (KNN), and naive Bayes (NB) approaches. It is shown that the SVM achieves higher modulation identification on the entire range of signal-to-noise ratio level.
How to Extract Useful Randomness from Unreliable Sources

For more than 30 years, cryptographers have been looking for public sources of uniform randomness in order to use them as a set-up to run appealing cryptographic protocols without relying on trusted third parties. Unfortunately, nowadays it is fair to assess that assuming the existence of physical phenomena producing public uniform randomness is far from reality. It is known that uniform randomness cannot be extracted from a single weak source. A well-studied way to overcome this is to consider several independent weak sources. However, this means we must trust the various sampling processes of weak randomness from physical processes. Motivated by the above state of affairs, this work considers a set-up where players can access multiple potential sources of weak randomness, several of which may be jointly corrupted by a computationally unbounded adversary. We introduce SHELA (Somewhere Honest Entropic Look Ahead) sources to model this situation. We show that there is no hope of extracting uniform randomness from a SHELA source. Instead, we focus on the task of Somewhere-Extraction (i.e., outputting several candidate strings, some of which are uniformly distributed – yet we do not know which). We give explicit constructions of Somewhere-Extractors for SHELA sources with good parameters. Then, we present applications of the above somewhere-extractor where the public uniform randomness can be replaced by the output of such extraction from corruptible sources, greatly outperforming trivial solutions. The output of somewhere-extraction is also useful in other settings, such as a suitable source of random coins for many randomized algorithms. In another front, we comprehensively study the problem of Somewhere-Extraction from a weak source, resulting in a series of bounds. Our bounds highlight the fact that, in most regimes of parameters (including those relevant for applications), SHELA sources significantly outperform weak sources of comparable parameters both when it comes to the process of Somewhere-Extraction, and in the task of amplification of success probability in randomized algorithms. Moreover, the low quality of somewhere-extraction from weak sources excludes its use in various efficient applications.
Information Modeling of Dual Education in the Field of IT

Modern approaches to the organization of the educational process generate a need for the introduction of dual education. Such approach is particularly relevant for IT specialties. The main principles of dual education are presented and it is suggested to use the information model of a dual education applicant as a set of knowledge about a person used for organization of effective learning processes, as well as the thematic subject model of a dual education applicant, the functional object model of a dual education applicant, the semantic subject model of a dual education applicant, the procedural subject model of a dual education applicant. It is provided the model of dual education that consists of the following models such as a model of educational material, test question models, models of reference knowledge of an expert (a teacher), a model of knowledge and a model of the practical component of the educational process.
CAQL: Continuous Action Q-Learning

Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such as games and recommender systems (RSs). When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient. However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem. While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation. Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard. In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers. In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution. To speed up training of CAQL, we develop three techniques, namely (i) dynamic tolerance, (ii) dual filtering, and (iii) clustering. To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy. To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.
Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin

For linear classifiers, the relationship between (normalized) output margin and generalization is captured in a clear and simple bound – a large output margin implies good generalization. Unfortunately, for deep models, this relationship is less clear: existing analyses of the output margin give complicated bounds which sometimes depend exponentially on depth. In this work, we propose to instead analyze a new notion of margin, which we call the “all-layer margin.” Our analysis reveals that the all-layer margin has a clear and direct relationship with generalization for deep models. We present three concrete applications of the all-layer margin: 1) by analyzing the all-layer margin, we obtain tighter generalization bounds for neural nets which depend on Jacobian and hidden layer norms and remove the exponential dependency on depth 2) our neural net results easily translate to the adversarially robust setting, giving the first direct analysis of robust test error for deep networks, and 3) we present a theoretically inspired training algorithm for increasing the all-layer margin and demonstrate that our algorithm improves test performance over strong baselines in practice.
Amrita-CEN-SentiDB 1: Improved Twitter Dataset for Sentimental Analysis and Application of Deep learning

Powerful weapon in today's world is ones emotion is social media. They have the power to make an individual trending overnight or even may pull down anyone reputation. In this paper, the sentiment analysis task has been performed by collecting the dataset from the publically available sources and by merging them together to form a new dataset for the sentiment analysis task that is positive or negative sentiment based on the context of the subject. A new reliable dataset is subjected to various pre-processing techniques and then the feature extraction techniques aftermath they are passed to the deep learning techniques out of which by using the text representation method, global vectors (glovec) with the long short-term memory (lstm) has the highest accuracy of 75%, which is the benchmark accuracy for this dataset. For the research purpose the dataset used in this paper is made available publically for research purpose.
Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living

Abnormal behaviour in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.
Graph-based Deep Learning in Natural Language Processing

This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.
Impact of geometrical restrictions in RANSAC sampling on the ID document classification

In this paper we explore the impact of geometrical restrictions in RANSAC sampling on the ID document type recognition accuracy in images, as well as on the accuracy of the projective distortion parameters estimation. The studied method is based on representing images as constellations of keypoints and their descriptors. The distortion parameters are estimated by applying RANSAC on the matched keypoints. Cases are studied where the base algorithm can yield erroneous or insufficiently accurate solution. A RANSAC scheme is presented with geometrical restrictors and several restriction are proposed, limiting the samples and the computed transform parameters. An experiment was conducted on the open dataset MIDV-500 and the data is presented of the dependence of classification and localization accuracy on the considered restrictors. It was shown that the introduction of restrictors allows to achieve a accuracy improvement and significant speed up.