Reviewerly/exHarmony huggingface dataset:
Improving CRNN with EfficientNet-like feature extractor and multi-head attention for text recognition

Text recognition is one of the most important and challenging tasks in image-based sequence recognition, which has various potential applications in real life. In this paper, we propose a novel convolutional-recurrent neural network (CRNN) for text recognition. Particularly, we adapt the EfficientNet architecture for extracting deep features and propose multi-head attention mechanisms to improve character localization. The experiments show that our EfficientNet-like feature extractor clearly outperforms other previous CNN feature extractors like VGG and ResNet. In overall, our proposed method yields competitive performance in comparison with other state-of-the-art approaches. Specifically, our F1-score is equivalent to top 3 on the ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction.
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the area of global optimization algorithms. Readily available algorithms are typically designed to be universal optimizers and, thus, often suboptimal for specific tasks. We propose a novel transfer learning method to obtain customized optimizers within the well-established framework of Bayesian optimization, allowing our algorithm to utilize the proven generalization capabilities of Gaussian processes. Using reinforcement learning to meta-train an acquisition function (AF) on a set of related tasks, the proposed method learns to extract implicit structural information and to exploit it for improved data-efficiency. We present experiments on a sim-to-real transfer task as well as on several simulated functions and two hyperparameter search problems. The results show that our algorithm (1) automatically identifies structural properties of objective functions from available source tasks or simulations, (2) performs favourably in settings with both scarse and abundant source data, and (3) falls back to the performance level of general AFs if no structure is present.
Deep Learning Based Extractive Text Summarization: Approaches, Datasets and Evaluation Measures

Recently, the number of online documents witness huge increase in volume. Thus, these documents need to be summarized in order to be effective. This paper reviews the most recent extractive text summarization approaches that are based on deep learning techniques. These approaches are classified into three categories based on deep learning techniques which are Restricted Boltzmann Machine, Variation Auto-Encoder and Recurrent Neural Network. The mostly used datasets for extractive summarizations are Daily Mail and DUC2002. Moreover, ROUGE is the mainly used evaluation measure to assess the quality of the extractive summarization process. The results show that SummaRuNNer approach which is based on Gated Recurrent Unit Recurrent Neural Network achieved the highest values for ROUGE1, ROUGE2 and ROUGE-L over Daily Mail datasets. On the other hand, the approach that is based on Recurrent Neural Network achieved the best results over DUC2002 datasets in term of ROUGE1 and ROUGE2.
Shark Smell Optimization (SSO) Algorithm for Cloud Jobs Scheduling

Cloud computing is an emerging technology based on moving the computing operations from the desktop computers to cloud providers through the internet. Typically, cloud providers on the internet process huge number of computing requests or jobs from enormous clients. Jobs scheduling in cloud computing is considered a crucial issue needs to be tackled. There are several methods available for jobs scheduling on cloud computing. However, there is a need for new mechanisms to reduce the job execution time and improve scheduling job process. This paper proposed a new job scheduling mechanism based on Shark Smell Optimization (SSO) algorithm. The proposed mechanism of job scheduling aims to find an efficient distribution of jobs based on SSO to mimic the behavior of sharks in the hunt wounded fish. Scheduling job in the proposed mechanism creates at first set of functions and resources to generate blood points by assigning jobs to resources randomly and assessing the blood points to find the place of a large amount blood points. The second phase repeats the regeneration of blood points based on the behavior of sharks to produce the best schedule for the jobs is that has the minimum execution time for jobs. Two experiments have been conducted to evaluate the proposed mechanism. The first stage of the evaluation process is a simple implementation scenarios using Java language. The second stage was a full simulation using CloudSim simulator toolkit. Several experiments scenarios using different numbers of jobs and resources are conducted. The simulation results revealed that the proposed SSO mechanism had outperformed Firefly Algorithm regarding the jobs execution time.
Linear codes of 2-designs associated with subcodes of the ternary generalized Reed–Muller codes

In this paper, the 3-rank of the incidence matrices of 2-designs supported by the minimum weight codewords in a family of ternary linear codes considered in Ding and Li (Discret Math 340(10):2415–2431, 2017) are computed. A lower bound on the minimum distance of the ternary codes spanned by the incidence matrices of these designs is derived, and it is proved that the codes are subcodes of the 4th order generalized Reed–Muller codes.
Vision-Based People Counter Using CNN-Based Event Classification

This article proposes a convolutional neural network (CNN)-based people counter that classifies a given frame cube to a specific event that indicates people entering or exiting a target area to measure instantaneous people count. For the training of the proposed CNN, a training input frame cube and its corresponding class label that represents a specific event are generated using the proposed counting rules. For mitigating the overfitting problem that may occur in the training of the proposed CNN, data augmentation, and postclass correction using foreground distribution with event probabilities are applied. The experimental results indicate that the proposed method improved the F1 score and accuracy for the cumulative people counting results by up to 9.0% and 14.8%, respectively, compared with those of the benchmark methods, even though it calculated the cumulative count by summing instantaneous people counts, while the benchmark methods were optimized for the calculation of the cumulative count.
A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.
Convolutional autoencoder and conditional random fields hybrid for predicting spatial-temporal chaos

We present an approach for data-driven prediction of high-dimensional chaotic time series generated by spatially-extended systems. The algorithm employs a convolutional autoencoder for dimension reduction and feature extraction combined with a probabilistic prediction scheme operating in the feature space, which consists of a conditional random field. The future evolution of the spatially-extended system is predicted using a feedback loop and iterated predictions. The excellent performance of this method is illustrated and evaluated using Lorenz-96 systems and Kuramoto-Sivashinsky equations of different size generating time series of different dimensionality and complexity.
Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain

Statistical analysis of health data is an essential task in healthcare. However, existing healthcare systems are incompatible with this critical need due to privacy restrictions. A recently emerged technology called Blockchain has shown great promise for mitigating this incompatibility. In this work, we aim to improve existing secure statistical analysis protocols by leveraging the blockchain technology. We propose a novel method that enables researchers to perform statistical analysis on health data in a privacy-preserving, secure, and precise manner.
Comparison of Algorithms and Technologies 2G, 3G, 4G and 5G

Technology has always been on the agenda of important world events and continues to be the main topic with perspective in the future as well. The practical relevance of the topic is about understanding the speeds that different networks have on different mobile phones whether Android or iPhone with proportions each different from each other. This paper discusses each generation characteristics and provides more accurate visions for technology development and the advancement of new technologies ranging from 2G to the latest as the most advanced 5G. Analysis and the difference of 2G, 3G, 4G and 5G Technologies and algorithms is made. In the end, an experiment using Android and IOS Smartphones is made as related to their download speed of three different files including a video, a picture and a music file.
Cloud Services and Pricing Strategies for Sustainable Business Models: Analytical and Numerical Approaches

Previous studies have introduced different potential pricing strategies for cloud services. However, not much research has been done comparing subscription pricing and pay-per-use pricing, which are commonly used pricing schemes. Also, there are very few studies which analyze a two-part tariff pricing scheme for cloud services, even though this option may increasingly attract service providers as the cloud market becomes more competitive and the profit margin grows narrower. Previous research has focused on firms’ profitability rather than social welfare due to the limitations of free services. This study uses theoretical and numerical analysis to compare the social welfare and profitability of three pricing schemes commonly used by firms: subscription pricing, pay-per-use pricing, and two-part tariff pricing. It shows that the pay-per-use pricing is the best solution from the perspective of social welfare, which contrasts with the conclusion of a previous study stating that social welfare is maximized under a two-part tariff. This paper also shows that the two-part tariff is the most profitable pricing scheme for firms.
Software Development Risk Modeling

The paper provides the method of software development risk modeling that allows evaluating the adverse situations during the implementation of software project phases, and enables the development of strategies and tactics of anticipation, perception and overcoming negative consequences of their manifestation. The authors have designed the model for determining the average value of the probability of potential risk events in developing the appropriate set of software based on trained experts' qualifications and appropriate for both each particular set and for the total probability of potential risk events in general. We have also formulated classification rules of potential risk events according to the probability of their occurrence.
Design and simulation of quantum image binarization using quantum comparator

Compared with classical image processing, quantum image processing provides a possible solution for faster image processing, which has been widely concerned. Quantum image binarization is a basic operation and plays an important role in image processing. Hence, we proposed an efficient design of quantum image binarization using quantum comparator. To reduce quantum cost and quantum delay, the comparator was optimized by rearranging the quantum gates. Then, a complete circuit implementation of quantum image binarization was designed using the comparator. Furthermore, we analyzed the performance of our design in terms of quantum cost, quantum delay and ancillary bits. Finally, the simulation verifies the correctness of our design.
Simulation-Based Algorithms for Markov Decision Processes: Monte Carlo Tree Search from AlphaGo to AlphaZero

AlphaGo and its successors AlphaGo Zero and AlphaZero made international headlines with their incredible successes in game playing, which have been touted as further evidence of the immense potential of artificial intelligence, and in particular, machine learning. AlphaGo defeated the reigning human world champion Go player Lee Sedol 4 games to 1, in March 2016 in Seoul, Korea, an achievement that surpassed previous computer game-playing program milestones by IBM’s Deep Blue in chess and by IBM’s Watson in the U.S. TV game show Jeopardy. AlphaGo then followed this up by defeating the world’s number one Go player Ke Jie 3-0 at the Future of Go Summit in Wuzhen, China in May 2017. Then, in December 2017, AlphaZero stunned the chess world by dominating the top computer chess program Stockfish (which has a far higher rating than any human) in a 100-game match by winning 28 games and losing none (72 draws) after training from scratch for just four hours! The deep neural networks of AlphaGo, AlphaZero, and all their incarnations are trained using a technique called Monte Carlo tree search (MCTS), whose roots can be traced back to an adaptive multistage sampling (AMS) simulation-based algorithm for Markov decision processes (MDPs) published in Operations Research back in 2005 [Chang, HS, MC Fu, J Hu and SI Marcus (2005). An adaptive sampling algorithm for solving Markov decision processes. Operations Research, 53, 126–139.] (and introduced even earlier in 2002). After reviewing the history and background of AlphaGo through AlphaZero, the origins of MCTS are traced back to simulation-based algorithms for MDPs, and its role in training the neural networks that essentially carry out the value/policy function approximation used in approximate dynamic programming, reinforcement learning, and neuro-dynamic programming is discussed, including some recently proposed enhancements building on statistical ranking &amp; selection research in the operations research simulation community.
Towards Facial De-Expression and Expression Recognition in the Wild

Previous work has shown that a facial expression is able to be decomposed into an expressive component and an neutral component. Many researchers have utilized this principle to implement the facial expression classification with paired laboratory-controlled datasets where each expression image has a corresponding neutral face image of the same identity. However, existing approaches cannot deal with unpaired in-the-wild datasets where most of the expression images lack their corresponding neutral counterparts. In this paper, we propose a new de-expression method based on domain transfer techniques that applies the above principle to unpaired in-the-wild datasets. Different expressions are treated as different image domains so that domain transfer techniques (such as generative adversarial networks) are able to be adopted to generate neutral face images for in-the-wild datasets. In such way, we obtain better expression recognition accuracy and alleviate the problem of lacking paired expression images. Experimental results conducted on three well-known public datasets, CK+, RAF and FER2013, validate the effectiveness of our proposed method. The results shows that our proposed approach outperforms the state-of-the-art methods on both laboratory-controlled and in-the-wild datasets.
Multi-dimensional belief fusion of multi-Gaussian structures

This paper describes the mathematical formulation of belief fusion of multi-Gaussian probability distribution functions (PDFs) in N-D, as well as the construction of some useful non-Gaussian structures. An emphasis of this work is the development of concise algorithms for constructing and efficiently fusing these non-Gaussian structures in N-space. In order to address decision-making using multi-Gaussian PDFs, an efficient probabilistic decision-making scheme is introduced and validated here. We investigate the trade-off between precision and efficiency in comparison with spatially discretizing fusion methods, concluding that the proposed techniques offer an improvement in both accuracy and efficiency for many contexts requiring non-Gaussian representation of belief. The proposed framework can be implemented in a diverse range of scenarios, potentially with real-time capability, and often without substantial sacrifice in accuracy.
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors

The accuracy of medical imaging-based diagnostics is directly impacted by the quality of the collected images. A passive approach to improve image quality is one that lags behind improvements in imaging hardware, awaiting better sensor technology of acquisition devices. An alternative, active strategy is to utilize prior knowledge of the imaging system to directly post-process and improve the acquired images. Traditionally, priors about the image properties are taken into account to restrict the solution space. However, few techniques exploit the prior about the noise properties. In this paper, we propose a neural network-based model for disentangling the signal and noise components of an input noisy image, without the need for any ground truth training data. We design a unified loss function that encodes priors about signal as well as noise estimate in the form of regularization terms. Specifically, by using total variation and piecewise constancy priors along with noise whiteness priors such as auto-correlation and stationary losses, our network learns to decouple an input noisy image into the underlying signal and noise components. We compare our proposed method to Noise2Noise and Noise2Self, as well as non-local mean and BM3D, on three public confocal laser endomicroscopy datasets. Experimental results demonstrate the superiority of our network compared to state-of-the-art in terms of PSNR and SSIM.
A Service Architecture Using Machine Learning to Contextualize Anomaly Detection

This article introduces a service that helps provide context and an explanation for the outlier score given to any network flow record selected by the analyst. The authors propose a service architecture for the delivery of contextual information related to network flow records. The service constructs a set of contexts for the record using features including the host addresses, the application in use and the time of the event. For each context the service will find the nearest neighbors of the record, analyze the feature distributions and run the set through an ensemble of unsupervised outlier detection algorithms. By viewing the records in shifting perspectives one can get a better understanding as to which ways the record can be considered an anomaly. To take advantage of the power of visualizations the authors demonstrate an example implementation of the proposed service architecture using a linked visualization dashboard that can be used to compare the outputs.
Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking

Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CNN) approach to detect the soccer ball in an image sequence. In contrast to the existing methods where only the current frame or an image is used for the detection, we make use of the history of frames. Using history allows to efficiently track the ball in situations where the ball disappears or gets partially occluded in some of the frames. Our approach exploits spatio-temporal correlation and detects the ball based on the trajectory of its movements. We present our results with three convolutional methods, namely temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve the detection task for an image using fully convolutional encoder-decoder architecture, and later, we use it as an input to our temporal models and jointly learn the detection task in sequences of images. We evaluate all our experiments on a novel dataset prepared as a part of this work. Furthermore, we present empirical results to support the effectiveness of using the history of the ball in challenging scenarios.
A Novel Cryptocurrency Wallet Management Scheme Based on Decentralized Multi-Constrained Derangement

Secure and stable cryptocurrency key management is important in modern cryptocurrency because the keys are the only way to access digital assets. Although many cryptocurrency wallet schemes have been proposed, some application limitations and inherent security risks still exist. In this paper, we propose a novel cryptocurrency wallet management scheme based on Decentralized Multi-Constrained Derangement (DMCD) to store the keys securely and stably in a decentralized network. Serving as the data distribution strategy, DMCD has high data dispersion and a better balance between the rate of storage space utilization and contribution, which can guarantee the security and stability of the key storage and recovery. In our scheme, to cope with the problem that nodes are frequently online and offline in the decentralized network, we employ a Shamir-Kademlia-Neighbor (SKN) redundancy strategy to ensure the high availability of stored key. Meanwhile, for achieving anonymous communication during DMCD data distribution, based on the Kademlia protocol, we change the Client/Server (C/S) mode of Hordes protocol to a decentralized version. All the proposed technologies can ensure that our scheme works well in a decentralized mode. The experiments and evaluations demonstrate that our scheme is efficient, stable, and secure in the decentralized network.