Reviewerly/exHarmony huggingface dataset:
Energy efficient routing and lightpath management in software defined networking based inter-DC elastic optical networks

To improve flexibility and robustness of high speed optical communication in inter-data center (DC) networks, Elastic optical networks (EONs) are progressively used. With continuous increase in the inter-DC traffic, it has become necessary to address the issue of rising power consumption for EONs. In this paper, we elaborately discussed the power consumption model of inter-DC EONs and the traffic prediction methods with central software defined networking (SDN) controller. The whole power consumption of the EON is controlled by avoiding the unnecessary tearing down and reactivating the lightpaths through the proposed Adaptive ant colony optimization based Deep echo state network (AACO-DESN) algorithm. The simulation results confirm the superiority of the proposed AACO-DESN algorithm in comparison to other existing algorithms in terms of energy efficiency and spectral efficiency for large EONs.
Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle

In this paper, predictive models of hourly global solar radiation (HGSR) at one-hour step ahead have been developed by adopting a new methodology. It consists of the association of a supervised machine learning algorithm named Support Vector Machine (SVM) with time series principle. A dataset of HGSR, collected at Ghardaïa city in the south of Algeria, has been used. In order to evaluate the accuracy of the developed models, we have used the Kolmogorov–Smirnov Integral (KSI) in addition to the conventional metrics (RMSE, NRMSE, NMBE, MAPE, and R). The results showed that accurate forecasts of HGSR at one-hour step ahead have been obtained by considering only its previous values at two-step forward. The predictive performances of the selected SVM model have been compared with those of some models available in the literature. This work proved the ability of the investigated machine learning algorithm with time series principle in predicting HGSR with good accuracy.
Object Vehicle Tracking by Convex Interpolation with Radar Accuracy

In this paper, we propose a method to reduce the object vehicle tracking error of the radar system. A radar sensor is suitable as a vehicle sensor, but it has measurement uncertainty due to the operating mechanism and reflection points. To cope with these problems, we divide the zones uniformly and use relative location information to identify surrounding vehicles and then analyze the error characteristics of each zone. As a result of analyzing, the accuracy of the radar was different depending on the object vehicle position. Then, we applied a convex interpolation to consider radar accuracy. We used the convex interpolation method to effectively track the object. The resulting estimate is more accurate than the radar measurement. We confirmed the utility of the proposed method through a scenario-based object vehicle tracking experiment.
Large-Scale Multi-modal Distance Metric Learning with Application to Content-Based Information Retrieval and Image Classification

Metric learning algorithms aim to make the conceptually related data items closer and keep dissimilar ones at a distance. The most common approach for metric learning on the Mahalanobis method. Despite its success, this method is limited to find a linear projection and also suffer from scalability respecting both the dimensionality and the size of input data. To address these problems, this paper presents a new scalable metric learning algorithm for multi-modal data. Our method learns an optimal metric for any feature set of the multi-modal data in an online fashion. We also combine the learned metrics with a novel Passive/Aggressive (PA)-based algorithm which results in a higher convergence rate compared to the state-of-the-art methods. To address scalability with respect to dimensionality, Dual Random Projection (DRP) is adopted in this paper. The present method is evaluated on some challenging machine vision datasets for image classification and Content-Based Information Retrieval (CBIR) tasks. The experimental results confirm that the proposed method significantly surpasses other state-of-the-art metric learning methods in most of these datasets in terms of both accuracy and efficiency.
Anti-stain algorithm of angular displacement based on a single image sensor

The displacement measuring technique is prone to failure within the industrial environment due to the influence of dust, oil, and other contaminants that stain the equipment. There is urgent demand for new anti-stain techniques. In today's image angular displacement measurement technology, the pixel array is used instead of the traditional photoelectric conversion element; this creates room for anti-stain improvement based on the image processing components. Based on a previous study on image-type angular displacement measurement technology, a single head image-type anti-stain algorithm is proposed in this paper that can remove the interference of small stains and ensure correct measurement value outputs. The influence of the stain on the calibration grating is first assessed based on the principle of image angular displacement measurement technology. An anti-stain algorithm based on the metal grating and multi-line fusion is proposed accordingly. The proposed algorithm is then tested on a circular grating with 38 mm diameter and ${{2}^{N}}\; = \;{256}$2N=256 lines in the circle. The results show that angle measurement output accuracy can be guaranteed when the number of lines covered by the stains is less than half of the coding-bits. This work may provide a technical basis for enhancing stain resistance in high-performance displacement measurement technology.
A Diffusion Theory for Deep Learning Dynamics: Stochastic Gradient Descent Escapes From Sharp Minima Exponentially Fast.

Stochastic Gradient Descent (SGD) and its variants are mainstream methods for training deep networks in practice. SGD is known to find a flat minimum that often generalizes well. However, it is mathematically unclear how deep learning can select a flat minimum among so many minima. To answer the question quantitatively, we develop a density diffusion theory (DDT) to reveal how minima selection quantitatively depends on the minima sharpness and the hyperparameters. To the best of our knowledge, we are the first to theoretically and empirically prove that, benefited from the Hessian-dependent covariance of stochastic gradient noise, SGD favors flat minima exponentially more than sharp minima, while Gradient Descent (GD) with injected white noise favors flat minima only polynomially more than sharp minima. We also reveal that either a small learning rate or large-batch training requires exponentially many iterations to escape from minima in terms of the ratio of the batch size and learning rate. Thus, large-batch training cannot search flat minima efficiently in a realistic computational time.
A Review of Steganography Techniques for Digital Information Transmission for Secure Channels with Digital Images

The methods of hiding information developed for steganography have evolved in the direction of increasing the volume of embedded data, imperceptibility and reliability, preserving the quality of the digital image to go unnoticed. The essential methods combined symmetric or asymmetric cryptography to find security techniques. The purpose of this paper is to show different approaches used for steganography; an exciting combination of mathematical algorithms is taught to keep the essential principles of hiding information. This paper shows a compilation on the development and application of different steganography methods that have been presented in the area of information security to analyze the progress that has been made, encompassing validation methods of steganography techniques, which include metrics of quality such as PSNR, MSE, SSIM among others and statistical techniques such as analysis by histogram, RS, chi-square, among others; these techniques are great importance because they set the standard to verify the reliability and robustness of signal that contains hidden message.
Small Ship Detection on Optical Satellite Imagery with YOLO and YOLT

Actually, the use of deep learning in object detection gives good results, but this performance decreases when there are small objects in the image. In this work, is presented a comparison between the last version of You Only Look Once (YOLO) and You Only Look Twice (YOLT) on the problem of detecting small objects (ships) on optical satellite imagery. Two datasets were used: High-Resolution Ship Collection (HRSC) and Mini Ship Data Set (MSDS), the last one was built by us. The mean object’s width for HRSC and MSDS are 150 and 50 pixels, respectively. The results showed that YOLT is good only for small objects with 76,06% of Average Precision (AP), meanwhile, YOLO reached 69,80% in the MSDS dataset. Moreover, in the case of the HRSC dataset where have objects of different sizes, YOLT obtained a 40% of AP against 75% of YOLO.
A review on outlier/anomaly detection in time series data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.
Multicultural Education as a Framework for Educating English Language Learners

This article discusses the value of multicultural education as a framework for guiding the teaching and learning process in post-secondary education. The focus centers on English Language Learners in the United States throughout all disciplines. An analysis of the five dimensions of Critical Multicultural Education (Banks, 2019) is given along with further commentary on key areas of focus to best guide both English Language Learners and native-born students to a better understanding of the course content and each other. The ideas from this article serve as a starting place for faculty members and higher education administrators from all over the world and across disciplines who seek to re-conceptualize their classrooms and/or the college or university experience to accommodate the ever-diversifying population of students via research in multicultural education.
Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance.
On The Reasons Behind Decisions.

Recent work has shown that some common machine learning classifiers can be compiled into Boolean circuits that have the same input-output behavior. We present theory for unveiling the reasons behind the decisions made by Boolean classifiers and study some of its theoretical and practical implications. We define notions such as sufficient, necessary and complete reasons behind decisions, in addition to classifier and decision bias. We show how these notions can be used to evaluate counterfactual statements such as a decision will stick even if ... because ... . We present efficient algorithms for computing these notions, which are based on new advances on tractable Boolean circuits, and illustrate them using case study.
Self-Tuning Deep Reinforcement Learning

Reinforcement learning (RL) algorithms often require expensive manual orautomated hyperparameter searches in order to perform well on a new domain.This need is particularly acute in modern deep RL architectures which oftenincorporate many modules and multiple loss functions. In this paper, we take astep towards addressing this issue by using metagradients (Xu et al., 2018) totune these hyperparameters via differentiable cross validation, whilst theagent interacts with and learns from the environment. We present theSelf-Tuning Actor Critic (STAC) which uses this process to tune thehyperparameters of the usual loss function of the IMPALA actor criticagent(Espeholt et. al., 2018), to learn the hyperparameters that defineauxiliary loss functions, and to balance trade offs in off policy learning byintroducing and adapting the hyperparameters of a novel leaky V-trace operator.The method is simple to use, sample efficient and does not require significantincrease in compute. Ablative studies show that the overall performance of STACimproves as we adapt more hyperparameters. When applied to 57 games on theAtari 2600 environment over 200 million frames our algorithm improves themedian human normalized score of the baseline from 243% to 364%.
Cybercrime in Europe: surprising results of an expensive lapse

Purpose The purpose of this paper is to investigate the impact of ransomware cyber-attacks “WannaCry” and “Petya” on stock prices of publicly traded companies in the European Union. The study analyses a set of case studies related to largest recent cybercrime events, which happened in the first half of 2017. The study answers two questions, what is the impact of cybercrime to public companies? How do cybercrime announcements and publications affect stock prices? Design/methodology/approach Using archival financial data, an event study methodology was used to assess the impact of cybercrime activity on market value of European companies affected during WannaCry and Petya ransomware attacks in 2017. Findings The results suggest that announcements of information breaches because of ransomware exploits have impact on stock market returns. There is evidence of positive investors` reactions to the announcements. Specifically, there was little impact of “Wannacry” ransomware attack on market returns. Although stock market reactions differ by the sector, the market was positively affected in general. Our analysis of the impact of the more aggressive “Petya attack,” aimed at destroying affected data found evidence that such information security breach leads to increased market returns. There were significant abnormal returns starting from the third day of the announcement. These findings contradict previous results and the literature related to the impact of cyber-attacks. Originality/value Contrary to previous findings, the results suggest that ransomware attacks lead to positive market returns. However, cybercrime and other types of cyber-attacks pose serious threats whose implications deserve further investigation. Different attacks may have different consequences and could be potentially damaging to a firm’s reputation. Thus, it is necessary for companies to avoid becoming victim of cybercrime. Information systems should be continuously monitored for vulnerabilities.
Anneal-path correction in flux qubits

Abstract Quantum annealers require accurate control and optimized operation schemes to reduce noise levels, in order to eventually demonstrate a computational advantage over classical algorithms. We study a high coherence four-junction capacitively shunted flux qubit (CSFQ), using dispersive measurements to extract system parameters and model the device. Josephson junction asymmetry inherent to the device causes a deleterious nonlinear cross-talk when annealing the qubit. We implement a nonlinear annealing path to correct the asymmetry in situ, resulting in a substantial increase in the probability of the qubit being in the correct state given an applied flux bias. We also confirm the multi-level structure of our CSFQ circuit model by annealing it through small spectral gaps and observing quantum signatures of energy level crossings. Our results demonstrate an anneal-path correction scheme designed and implemented to improve control accuracy for high-coherence and high-control quantum annealers, which leads to an enhancement of success probability in annealing protocols.
Preliminary study of improved median filter using adaptively mask size in light microscopic image

Abstract This study aimed to develop and evaluate an improved median filter (IMF) with an adaptive mask size for light microscope (LM) images. We acquired images of the mouse first molar using a LM at 100× magnification. The images obtained using our proposed IMF were compared with those from a conventional median filter. Several parameters such as the contrast-to-noise ratio, coefficient of variation, no-reference assessments and peak signal-to-noise ratio were employed to evaluate the image quality quantitatively. The results demonstrated that the proposed IMF could effectively de-noise the LM images and preserve the image details, achieving a better performance than the conventional median filter.
Bangla Continuous Handwriting Character and Digit Recognition Using CNN

There are several works in Bangla handwritten character recognition. Here a new methodology proposed to recognize the character from continuous Bangla handwritten character. The system’s main components are preprocessing, feature extraction, and recognition. There is a strong possibility that is found in Bangla words, and characters are overlapped. This problem often happens in handwritten texts like a consecutive character appears on another character. When it comes to Bangla characters, segmentation becomes much more difficult. To build an effective OCR system of Bangla handwritten text, recognition of characters is important as much as segmentation of characters. Here the main purpose is creating a system, which takes continuous Bangla handwritten text images as an input and then segments the input texts into its constituent words and finally segments each word into individual characters. In this present study, here we used EkushNet dataset model which includes 50 basic characters, 10 character modifiers, 52 frequently used conjunct characters, and 10 digits. By using our algorithm, we are able to segment 95% words from text and 90% characters from the words. Overall, in this present OCR system here recognition and segmentation of characters from handwritten Bangla texts are effectively dealing with the probable problems.
Long-term deposits prediction: a comparative framework of classification model for predict the success of bank telemarketing

The long-term deposits product is often offered to prospective customers by the bank through telemarketing strategy. How to maximize customer value through telemarketing strategy is a major focus in this research. Therefore, required a model that can classify potential customers with the potential to increase corporate earnings. The Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), K-Nearest Neighbour (K-NN), Support Vector Machine (SVM), Neural Network (NN), and Logistic Regression (LR) model have been proposed to compare. The comparison of these algorithms evaluated using the Protestal Bank dataset of the UCI Machine Learning repository, for performance algorithms using Area Under Curve (AUC) and Accuracy. From the experiment, the results show that SVM yield promising results with the Accuracy and AUC 97.07% and 0.925 respectively. It can be concluded that SVM is the best choice for classifying prospective customers who have the potential to be interested in time deposit products that are offered by telephone or cellular as distinguished from other classification algorithms.
Privacy and Ledger Size Analysis for Healthcare Blockchain

Since personal health information is sensitive data, it should not be accessible to anyone. Only authorized users should be able to see it. Blockchain, new middleware platform paradigm, can provide confidentiality and data sovereignty as well as transparency and traceability. Personal Health Record (PHR) incorporating blockchain can be managed by the patient themselves, not by the centralized institution, through blockchain. In this paper, PHR based on Hyperledger Fabric (consortium blockchain) is proposed. We also analyze and compare performance in terms of transaction delay and ledger size for private data access.
A Method of Efficient OTP Generation Using Pseudorandom Number Generators

Something you know based authentication method is the most classical authentication technique on the computer system, and is performed more easily and successfully than other methods even it is a vulnerable method against attacks such as eavesdropping or replay attack. To solve this problem, OTP (Onetime Password) technique is used. OTP is delivered from one side to the other in two approaches. In the first approach, OTP is generated on server side and sent to clients, for example, by SMS. In the second approach, OTP is generated on both sides at one time based on shared counter, such as, HOTP. In most cases, one-way hash functions are used in both approaches to generate OTP. As resent researches show the weakness of the hash functions, we need new a new OTP generation algorithms to solve this problem. In this paper, we propose a method of creating one time password based on pseudorandom number generators (PRNG), which they are exist on many systems and programming languages as native.
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping

Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction. One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint? Humans excel at this task. Our ability to imagine and fill in missing information is tightly coupled with perception: we feel as if we see the world in 3 dimensions, while in fact, information from only the front surface of the world hits our retinas. This paper explores the role of view prediction in the development of 3D visual recognition. We propose neural 3D mapping networks, which take as input 2.5D (color and depth) video streams captured by a moving camera, and lift them to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera. The model also projects its 3D feature maps to novel viewpoints, to predict and match against target views. We propose contrastive prediction losses to replace the standard color regression loss, and show that this leads to better performance on complex photorealistic data. We show that the proposed model learns visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating the motion of the inferred 3D feature maps in videos of dynamic scenes. To the best of our knowledge, this is the first work that empirically shows view prediction to be a scalable self-supervised task beneficial to 3D object detection.