Reviewerly/exHarmony huggingface dataset:
Learning Syllables Using Conv-LSTM Model for Swahili Word Representation and Part-of-speech Tagging

The need to capture intra-word information in natural language processing (NLP) tasks has inspired research in learning various word representations at word, character, or morpheme levels, but little attention has been given to syllables from a syllabic alphabet. Motivated by the success of compositional models in morphological languages, we present a Convolutional-long short term memory (Conv-LSTM) model for constructing Swahili word representation vectors from syllables. The unified architecture addresses the word agglutination and polysemous nature of Swahili by extracting high-level syllable features using a convolutional neural network (CNN) and then composes quality word embeddings with a long short term memory (LSTM). The word embeddings are then validated using a syllable-aware language model ( 31.267 ) and a part-of-speech (POS) tagging task ( 98.78 ), both yielding very competitive results to the state-of-art models in their respective domains. We further validate the language model using Xhosa and Shona, which are syllabic-based languages. The novelty of the study is in its capability to construct quality word embeddings from syllables using a hybrid model that does not use max-over-pool common in CNN and then the exploitation of these embeddings in POS tagging. Therefore, the study plays a crucial role in the processing of agglutinative and syllabic-based languages by contributing quality word embeddings from syllable embeddings, a robust Conv–LSTM model that learns syllables for not only language modeling and POS tagging, but also for other downstream NLP tasks.
A Driver’s Visual Attention Prediction Using Optical Flow

Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features.
Scene Recognition by Joint Learning of DNN from Bag of Visual Words and Convolutional DCT Features

Scene recognition is used in many computer vision and related applications, including information retrieval, robotics, real-time monitoring, and event-classification. Due to the complex nature of the task of scene recognition, it has been greatly improved by deep learning architectures that can be trained by utilizing large and comprehensive datasets. This paper presents a scene classification method in which local and global features are used and are concatenated with the DCT-Convolutional features of AlexNet. The features are fed into AlexNet's fully connected layers for classification. The local and global features are made efficient by selecting the correct size of Bag of Visual Words (BOVW) and feature selection techniques, which are evaluated in the experimentation section. We used AlexNet with the modification of adding additional dense fully connected layers and compared its result with the model previously trained on the Places365 dataset. Our model is also compared with other scene recognition methods, and it clearly outperforms in terms of accuracy.
Data and the Fourth Industrial Revolution

Abstract Ron S. Kenett and Shirley Y. Coleman outline the roles played by data and statistics in “Industry 4.0”, from monitoring manufacturing processes to the building of “digital twins”
A generic FPGA-based hardware architecture for recursive least mean p-power extreme learning machine

Recursive least mean p-power extreme learning machine (RLMP-ELM) is a newly proposed online machine learning algorithm and is able to provide a robust online prediction of the datasets with noises of different statistics. To further explore the proposed RLMP-ELM to be used in real-world embedded systems, a generic serial FPGA-based hardware architecture of RLMP-ELM is presented in this paper. The entire hardware architecture of RLMP-ELM includes three serial processing modules, which are implemented parameterizably and can be adapted for different application requirements. The hardware framework is in a serial fashion, but parallelization efforts are focused on the processes with high computing complexity by analysis of potential inter-task dependency. To overcome the limitation of memory bandwidth, the block RAM and ping-pong on-chip buffer are applied to improve the computational throughput. The validation experiments are performed through five datasets with different p values. Accuracy results show that our implementation on FPGA could achieve similar accuracy compared to 64-bit floating-point software implementation. We also report and compare hardware performance of our proposed architecture with other existing implementations. The results show that our hardware architecture offers the excellent balance among accuracy, logic occupation and hardware performance.
Votenet++: Registration Refinement For Multi-Atlas Segmentation

Multi-atlas segmentation (MAS) is a popular image segmentation technique for medical images. In this work, we improve the performance of MAS by correcting registration errors before label fusion. Specifically, we use a volumetric displacement field to refine registrations based on image anatomical appearance and predicted labels. We show the influence of the initial spatial alignment as well as the beneficial effect of using label information for MAS performance. Experiments demonstrate that the proposed refinement approach improves MAS performance on a 3D magnetic resonance dataset of the knee.
Resilient monitoring in self-adaptive systems through behavioral parameter estimation

Cyber-physical systems need self-adaptation as a mean to autonomously deal with changes. For runtime adaptation, a cyber-physical system repeatedly monitors the environment for detecting possible changes. Faults in the monitoring devices due to the dynamic and uncertain environment is very likely, necessitating resilient monitoring. In this paper, we discuss imperfect monitoring in self-adaptive systems, and propose a model-driven methodology to represent the self-adaptive system using a parametric Markov decision process, where the changes are reflected by a set of model parameters. Fault in the monitoring device may result in some parameter valuation miss. We propose a comprehensive framework for parameter estimation using behavioral patterns of the system implemented by a pattern-matching component. The proposed method simulates the current behavior of the system using random walk patterns, and matches it to a history of patterns for estimating the valuation of the omitted data. The results show that the accuracy of the framework is evaluated about 96% under imperfect monitoring. In addition, we elaborate a set of theoretical proofs to support rigorous error analysis, and determine a certain upper-bound of errors to guarantee an accurate decision-making outcome. We establish a logical connection between the error and the accuracy of decisions, and introduce tolerable error metric that is used to guarantee the accuracy of decisions under estimation.
Adaptive window based fall detection using anomaly identification in fog computing scenario

Human fall detection is a subcategory of ambient assisted living. Falls are dangerous for old aged people especially those who are unaccompanied. Detection of falls as early as possible along with high accuracy is indispensable to save the person otherwise it may lead to physical disability even death also. The proposed fall detection system is implemented in the edge computing scenario. An adaptive window-based approach is proposed here for feature extraction because window size affects the performance of the classifier. For training and testing purposes two public datasets and our collected dataset have been used. Anomaly identification based on a support vector machine with an enhanced chi-square kernel is used here for the classification of Activities of Daily Living (ADL) and fall activities. Using the proposed approach 100% sensitivity and 98.08% specificity have been achieved which are better when compared with three recent research based on unsupervised learning. One of the important aspects of this study is that it is also validated on actual real fall data and got 100% accuracy. This complete fall detection model is implemented in the fog computing scenario. The proposed approach of adaptive window based feature extraction is better than static window based approaches and three recent fall detection methods.
Bayesian K-Nearest Neighbour based Redundancy Removal and Hand Gesture Recognition in Isolated Indian Sign Language without Materials Support

Abstract Indian sign language is used by deaf and dumb persons for communication. Physical materials are not needed for ISL. During this anticipated paper we tend to propose a unique vision-based (VB) methodology for the Indian language (ISL) recognition of isolated signs. The methodology proposed consists of 3 modules: preprocessing, extraction and classification of features. One signer hand is segmented from sign video in segmentation or preprocessing phase. Feature vector is extracted from feature extraction module and show the manual sign parameters used as input for classification. To decrease the computational complexity of the system, a redundant frame algorithmic rule has been applied. The experiment results demonstrate that proposed system achieves 90.3% recognition accuracy based on a lexicon of twenty-one signs of ISL.
K-means Clustering Based on Improved Quantum Particle Swarm Optimization Algorithm

In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.
ArtGAN: Artwork Restoration using Generative Adversarial Networks

We propose a method to recover and restore art-work that has been damaged over time due to several factors. Our method produces great results by completely removing damages in most of the images and perfectly estimating the damaged region. We achieved accurate results due to (i) a custom data augmentation technique which depicts realistic damages rather just blobs (ii) novel CResNetBlocks that subsequently upsample and downsample features to restore the image with efficient backpropagation measures, and (iii) the choice of using patch-discriminators to achieve sharpness and colorfulness. Our network architecture is a conditional Generative Adversarial Network where the generator uses a combination of adversarial loss, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss and the discriminator uses binary cross-entropy loss for optimization. While the expressiveness of existing comparison methods is limited, we present our results with several metrics for future comparison and showcase some visuals of recovered artwork. PyTorch implementation is available at: https://github.connamasl91297/artgan.
Know-Me: A Toolkit for Designing Personalised Dementia Care

Personalisation is a crucial element in providing person-centred care for people with dementia. This paper presents the development and evaluation of a design toolkit to facilitate the work of designers and healthcare professionals in personalising dementia care. This toolkit, named “Know-me”, was grounded in the findings of Ergonomics in Aging, Co-design, and Data-enabled Design, derived from literature review and from the field during a four-year doctorate project. “Know-me” was designed to be easily accessible, flexible, and engaging, providing concrete and hands-on guidance for designers and healthcare professionals to use in designing for personalised dementia care. A proof-of-concept evaluation of the “Know-me” toolkit was conducted via student projects on design for dementia care. During this process, we found that “Know-me” could be adapted flexibly so that the care team could use some of the tools by themselves. A feature-by-feature comparison of the “Know-me” toolkit with similar state-of-the-art toolkits was conducted, and based upon this, the strengths and weaknesses of the “Know-me” toolkit are discussed. This preliminary study indicates that the “Know-me” toolkit is a helpful addition to the current pool of toolkits on designing for dementia care.
Measurement backaction control of quantum dissipation in a nonlinear cavity-based Duffing oscillator

Quantum backaction from weak measurement affects the behavior of quantum systems. We consider a nonlinear driven Duffing oscillator system implementation in a nonlinear optical cavity, and we show that the choice of phase setting $\ensuremath{\phi}$ for a laser used in measurement can change the dissipation for the spread variables of the quantum state. This can considerably increase the energy absorbed via the energy channel of the spread variables and enhance quantum effects. This suggests novel applications to quantum control, as we demonstrate, including an example in which the energy in the spread variables allows for a dynamical tunneling between energetically separated dynamical stead-states.
iVolunteer - A Platform for Digitization and Exploitation of Lifelong Volunteer Engagement

Volunteering is an important cornerstone of our society, from social welfare to disaster relief, supported by a variety of volunteer management systems (VMS). These VMS focus primarily on centralized task management within non-profit organizations (NPOs) but generally do not provide mechanisms that allow volunteers to privately digitize and exploit their engagement assets in terms of digital badges for activities accomplished or competences acquired, in a trustful manner. This lack of sovereignty hampers volunteers in the exploitation of their engagement assets wrt. self-exploration, but also with regard to possible transfers of assets to other NPOs and beyond, e.g., to the education or labor market.
Discussion of “Currency Stability Using Blockchain Technology”

The volatility of crypto currencies hinders their ability to be media of exchange or stores of value, leading to the implementation of exchange-rate pegs in an attempt to stabilize these currencies. This strategy has been used by crypto currencies such as US Dollar Tether, Steem Backed Dollar and TrueUSD; and was previously adopted in countries such as Brazil, Mexico and Argentina. However, an exchange-rate peg is vulnerable to speculative attacks if it is not 100% backed by reserves, as discussed in Obstfeld (1996). Using insights from the bank-run literature, Routledge and Zetlin-Jones (2018) build on Green and Lin (2003) and propose a model of speculative attacks. They show that adjustments to the exchange rate can prevent speculative attacks in equilibrium. They also show how to implement such contracts using blockchain technology. In this discussion paper, I provide a cautionary tale. I show also in a version of Green and Lin (2003) that the information content in the blockchain prevents agents from attaining all the gains from risk sharing—highlighting the downsides of too much public information.
Quantified Analysis of Security Issues and Its Mitigation in Blockchain Using Game Theory

Storing data in the Blockchain is indeed one of the good security measures for data. However, blockchain itself could be under different types of security threats. The mining of the block into the longest chain is a constrained task. Typically, the nodes having high investments are selected as potential miners in the blockchain. A miner or a pool of miners is assigned for this mining job. The challenge lies in working with the honest miners against the continuous negative influence of dishonest miners. There have been considerable efforts in the existing literature that tries to overcome such security threats. Game theory is used and incorporated towards this by many researchers. This manuscript aims to analyze different security threats of blockchain mining and the possible approaches that have claimed to overcome these. We also analyzed and correlated some of the selected well-cited solution approaches that uses game theory and presented a comparative performance analysis among those.
AlphaBlock: An Evaluation Framework for Blockchain Consensus Algorithms

Consensus algorithm is the core of blockchain and it plays a crucial role in the performance of the blockchain. In general, there are two types of blockchain consensus algorithms: the Bitcoin-like Nakamoto consensus (NC) algorithms and the Byzantine fault tolerance (BFT) consensus algorithms. These two types of consensus algorithms are fundamentally different in forms and hard to be compared. However, currently, they are often used interchangeably for similar blockchains, which naturally raises a question of "given a network, which consensus would have the best performance in practice''. In this paper, we propose AlphaBlock, a theoretical framework for the performance comparison of blockchain consensus algorithms, in particular, NC algorithms and BFT algorithms. To make fair comparisons, AlphaBlock captures the most important advantages and disadvantages of both categories. Moreover, we incorporate some of the key features of the practical blockchain networks. The results show that BFT algorithms have a superior performance over NC algorithms in most cases in both throughput and latency, expect for the low latency region in large networks, where the NC algorithms show strong competence to the best BFT algorithms.
An Efficient Techniques for Fraudulent detection in Credit Card Dataset: A Comprehensive study

Abstract Now a day, credit card transaction is one the famous mode for financial transaction. Increasing trends of financial transactions through credit cards also invite fraud activities that involve the loss of billions of dollars globally. It is also been observed that fraudulent transactions have increased by 35% from 2018. A huge amount of transaction data is available to analyze the fraud detection activities that require analysis of behavior/abnormalities in the transaction dataset to detect and ignore the undesirable action of the suspected person. The proposed paper lists a compressive summary of various techniques for the classification of fraud transactions from the various datasets to alert the user for such transactions.
Reference-Based Texture Transfer For Single Image Super-Resolution Of Magnetic Resonance Images

Magnetic Resonance Imaging (MRI) is a valuable clinical diagnostic modality for spine pathologies with excellent characterization for infection, tumor, degenerations, fractures and herniations. However in surgery, image-guided spinal procedures continue to rely on CT and fluoroscopy, as MRI slice resolutions are typically insufficient. Building upon state-of-the-art single image super-resolution, we propose a reference-based, unpaired multi-contrast texture-transfer strategy for deep learning based in-plane and across-plane MRI super-resolution. We use the scattering transform to relate the texture features of image patches to unpaired reference image patches, and additionally a loss term for multicontrast texture. We apply our scheme in different super-resolution architectures, observing improvement in PSNR and SSIM for 4x super-resolution in most of the cases.
Adventures in Crypto Dark Matter: Attacks and Fixes for Weak Pseudorandom Functions

A weak pseudorandom function (weak PRF) is one of the most important cryptographic primitives for its efficiency although it has lower security than a standard PRF.
Trajectory prediction of ballistic missiles using Gaussian process error model

Ballistic Missile Trajectory Prediction (BMTP) is critical to air defense systems. Most Trajectory Prediction (TP) methods focus on the coast and reentry phases, in which the Ballistic Missile (BM) trajectories are modeled as ellipses or the state components are propagated by the dynamic integral equations on time scales. In contrast, the boost-phase TP is more challenging because there are many unknown forces acting on the BM in this phase. To tackle this difficult problem, a novel BMTP method by using Gaussian Processes (GPs) is proposed in this paper. In particular, the GP is employed to train the prediction error model of the boost-phase trajectory database, in which the error refers to the difference between the true BM state at the prediction moment and the integral extrapolation of the BM state. And the final BMTP is a combination of the dynamic equation based numerical integration and the GP-based prediction error. Since the trained GP aims to capture the relationship between the numerical integration and the unknown error, the modified BM state prediction is closer to the true one compared with the original TP. Furthermore, the GP is able to output the uncertainty information of the TP, which is of great significance for determining the warning range centered on the predicted BM state. Simulation results show that the proposed method effectively improves the BMTP accuracy during the boost phase and provides reliable uncertainty estimation boundaries.