Reviewerly/exHarmony huggingface dataset:
The evaluation of the photovoltaic potential in the urban environment Case of the Nador City / Morocco

Currently, around half of the world’s population is concentrated in cities, as indicated by UN forecasts (2014). These growths lead us to reflect on the development of cities and their impact on the environment. Indeed, the use of roofs that are often unusable spaces can contribute to the satisfaction of the energy needs of the city and its inhabitants. Using Nador City as a case study area, this project aims to study the possibility that the roofs are supplied with solar panels by delimiting the authorized roof areas. The procedure using in this study made it possible to obtain the regions with the greatest localization capacity of photovoltaic systems as well as an approximation of the energy produced according to solar radiation by comparing the expected results to be obtained with different photovoltaic modules. An important feature of this method is the use of a LIDAR data point cloud, allowing the creation of a digital surface model (DSM) of the study area which is the main source of information. In this way, a Boolean multi-criteria analysis was carried out based on the geographic information system (GIS) taking into account a series of criteria associated with spatial characteristics such as the shadow, the inclination and orientation, and the accessibility to roofs, without excluding the elimination of inadequate buildings. Statistical analysis indicates that 61% of the rooftop segments identified are suitable for photovoltaic systems with an annual production of electricity reaching 452.88 GWh which could meet the energy needs of the city and a considerable annual monetary gain estimated at 443.81 million dirhams. In general, this project allows urban planners and decision-makers to obtain a clear vision and to make decisions on realistic and sustainable objectives.
L1-Norm Method for Dual-Wavelength Phase Unwrapping

According to the relationship between the absolute phase and the baseline in multiple baselines, the relationship between the absolute phase and the band in the multi-band is designed analogously. This method constructs the equation that the wavelength and the absolute phase are positively correlated with the phase relationship of any pixel in different band images. Using the equivalence relation, two inequalities of adjacent pixels are constructed, the gradient information is obtained, the ambiguity is minimized, and the phase unwrapped 2π fuzzy number is obtained. This method avoids the limitation of the Itoh hypothesis. In this paper, two experiments are designed with two wavelengths. The results effectively improve the unwrapping ability of the phase inconsistent region compared with single wave unwrapping, which proves the feasibility of this analogy method.
Image Compression Based on a Hybrid Wavelet Packet and Directional Transform (HW&amp;DT) Method

In this paper, a Hybrid Wavelet Packet and Directional transform (HW&DT) method is proposed to compress an image effectively. The image is pixel de-correlated using Daubechies Wavelet Packet transform and set of wavelet packet coefficients are transformed using Directional transform. The Directional transform pairs DCT-II, DCT-V, DCT-VIII and DST-VII are useful in retrieving the texture information in different directions. Then the coefficients of the hybrid transform are uniformly quantized and entropy coded using Huffman coding, generating the Bit-Stream. The performance evaluation of the proposed work is done using the compression parameters like Structural Similarity Index (SSIM), Bit-Saving (%), Peak Signal to Noise ratio (PSNR) and Mean Square Error (MSE). The experimental results confirm the improvement in Bit-Saving for the image.
Considerations in Adding Adaptive Training to Existing Virtual Environments

Virtual environments (VEs) afford learners the ability to train in simulated versions of real-world environments or in novel environments to attain or refine skills. Performance within these training tasks are likely to benefit from appropriate feedback and assessments. As the usefulness of VEs and adaptive training technology become more valuable, recommendations and considerations on their integration are important to explore. There are opportunities to integrate these existing environments with adaptive training using a domain-independent intelligent tutoring system (ITS) framework, such as the Generalized Intelligent Framework for Tutoring (GIFT). This paper discusses considerations and approaches to adding intelligent tutoring to existing VEs to extend their usefulness and accessibility.
Intelligent generation algorithm of ceramic decorative pattern

Aim to resolve the problem of limited versatility of ceramic decorative pattern generation method. An intelligent generation algorithm of ceramic decorative patterns is proposed based on deep neural network learning, by using the data set about flowers prepared in advance to train the deeplab v3+ network model for semantic segmentation, along with the trained model to perform the image style transfer experiment, in order to generate a pattern similar to the decorative on ceramics. The experiment results show that the generated ceramic decorative pattern is closer to the real decorative pattern, and the effect is more realistic and practical. The proposed method enable to convert photos of ordinary flowers into ceramic decorative pattern, which has a better effect than using image style migration alone, and it also can realize the intelligent generation of ceramic decorative patterns on deep neural networks.
Text emotion analysis of BGRU model based on the fusion of emoticons

Micro-blog is a platform for users to get information and convey their own ideas. In recent years, the emotional analysis of micro-blog has gradually become a hot topic. The publication of micro blog not only includes text, but also emoticons are a part that cannot be ignored. Traditional research methods ignore the importance of emoticons to the emotional polarity of text when preprocessing the micro blog. This paper proposes a research method of text emotion analysis based on the fusion of emoticons. By micro-blog to crawl the data preprocessing, selected text in the emoticons, using emotional dictionary gives corresponding weights and calculate the score, then transform text into the corresponding word vector sequence, using Bidirectional Gated Recurrent Unit network context information text emotion tendency, finally selects the Conditional Random Field polarity judgment of text. The experimental results show that the accuracy of the proposed method is up to 89%.
Fast polynomial inversion for post quantum QC-MDPC cryptography

New post-quantum Key Encapsulation Mechanism (KEM) designs, evaluated as part of the NIST PQC standardization Project, pose challenging tradeoffs between communication bandwidth and computational overheads. Several KEM designs evaluated in Round-2 of the project are based on QC-MDPC codes. BIKE-2 uses the smallest communication bandwidth, but its key generation requires a costly polynomial inversion. In this paper, we provide details on the optimized polynomial inversion algorithm for QC-MDPC codes (originally proposed in the conference version of this work). This algorithm makes the runtime of BIKE-2 key generation tolerable. It brings a speedup of 11.4× over the commonly used NTL library, and 83.5× over OpenSSL. We achieve additional speedups by leveraging the latest Intel's Vector-PCLMULQDQ instructions, 14.3× over NTL and 103.9× over OpenSSL. Our algorithm and implementation were the reason that BIKE team chose BIKE-2 as the only scheme for its Round-3 specification (now called BIKE).
Design and Development of Hindrance Application Using Vocal and Quick Responsible Code for Railway

According to the fast-growing world, people are completely dependent of technology and mobile applications. In this regard, a hindrance is required at all levels to provide the information about railways through the specific application. This application provides limited services related to railways like train details, passenger name record (PNR) status, live status, train routes, and ticket bookings. This application holds the basic vocal model using Google assistance and functions on analog involvement of the user. The authenticity required for this application is done through quick responsible (QR) code. Through this application, people can perform the operations as mentioned above. The user and detailed information of tickets were purchased, and inquiry is gathered through their Aadhaar card #number submission through QR. The output of this application would be in the form of voice generated by the application. This application is built using Android Studio.
Local Neighbor Propagation Embedding

Manifold learning occupies a vital role in the field of nonlinear dimensionality reduction and serves many relevant methods. Typical linear embedding methods have achieved remarkable results. However, these methods perform poorly on sparse data. To address this issue, we introduce neighbor propagation into Locally Linear Embedding (LLE) and propose a new method named Local Neighbor Propagation Embedding (LNPE). LNPE enhances the local connections between neighborhoods by extending 1-hop neighbors into n-hop neighbors. The experimental results show that LNPE can obtain faithful embeddings with better topological and geometrical properties.
Fast and linear-time string matching algorithms based on the distances of $q$-gram occurrences

Given a text T of length n and a pattern P of length m, the string matching problem is a task to find all occurrences of P in T. In this study, we propose an algorithm that solves this problem in O((n + m)q) time considering the distance between two adjacent occurrences of the same q-gram contained in P. We also propose a theoretical improvement of it which runs in O(n + m) time, though it is not necessarily faster in practice. We compare the execution times of our and existing algorithms on various kinds of real and artificial datasets such as an English text, a genome sequence and a Fibonacci string. The experimental results show that our algorithm is as fast as the state-of-the-art algorithms in many cases, particularly when a pattern frequently appears in a text.
Higher Education Teaching Material on Machine Learning in the Domain of Digital Pathology

Machine learning (ML) and especially deep learning (DL) are doubtless one of the key technologies of the last couple of years and future decades. Learning its theoretical concepts alone is a big challenge as it requires a strong background in mathematics and computer science. Once students and researchers have built up on the core concepts and recent algorithms using toy data sets and want to move forward tackling real-world application scenarios, they are confronted with additional problems, specific to the application domain. Digital pathology is one of those domains that has many of these additional issues, such as: New file formats, large images, and the amount of medical expertise needed to capture the underlying problem is relatively large. In our project deep-teaching.org we provide teaching materials to introduce computer science students to the use of ML for digital pathology. As example, we present the application of pathologic N-stage (pN-stage) classification by dividing it into sub-tasks and propose - step by step - how different machine and deep learning, as well as computer vision techniques, can be combined to contribute to the main objective.
Unleashing Constraint Optimisation Problem solving in Big Data environments

The application of the optimisation problems in the daily decisions of companies is able to be used for finding the best management according to the necessities of the organisations. However, optimisation problems imply a high computational complexity, increased by the current necessity to include a massive quantity of data (Big Data), for the creation of optimisation problems to customise products and services for their clients. The irruption of Big Data technologies can be a challenge but also an important mechanism to tackle the computational difficulties of optimisation problems, and the possibility to distribute the problem performance. In this paper, we propose a solution that lets the query of a data set supported by Big Data technologies that imply the resolution of Constraint Optimisation Problem (COP). This proposal enables to: (1) model COPs whose input data are obtained from distributed and heterogeneous data; (2) facilitate the integration of different data sources to create the COPs; and, (3) solve the optimisation problems in a distributed way, to improve the performance. It is done by means of a framework and supported by a tool capable of modelling, solving and querying the results of optimisation problems. The tool integrates the Big Data technologies and commercial solvers of constraint programming. The suitability of the proposal and the development have been evaluated with real data sets whose computational study and results are included and discussed.
The Emergence of Digital Twins in Remediation: How Platform Synchrony Will Change the Model for Delivery

Groundwater Monitoring & RemediationVolume 40, Issue 3 p. 14-20 Advances in Remediation Solutions The Emergence of Digital Twins in Remediation: How Platform Synchrony Will Change the Model for Delivery John Horst, Corresponding Author John Horst john.horst@arcadis.com Search for more papers by this authorNick Welty, Nick WeltySearch for more papers by this authorAllison Yanites, Allison YanitesSearch for more papers by this authorFrancois Appere, Francois AppereSearch for more papers by this authorMonica Dupre, Monica DupreSearch for more papers by this authorShahansha Shaik, Shahansha ShaikSearch for more papers by this author John Horst, Corresponding Author John Horst john.horst@arcadis.com Search for more papers by this authorNick Welty, Nick WeltySearch for more papers by this authorAllison Yanites, Allison YanitesSearch for more papers by this authorFrancois Appere, Francois AppereSearch for more papers by this authorMonica Dupre, Monica DupreSearch for more papers by this authorShahansha Shaik, Shahansha ShaikSearch for more papers by this author First published: 26 June 2020 https://doi.org/10.1111/gwmr.12401Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume40, Issue3Summer 2020Pages 14-20 RelatedInformation
Empirical Study on the Influence of Security Control Management and Social Factors in Deterring Information Security Misbehaviour

Abstract Complying with the security rules and standard is important to safeguard valuable information in the organisation. Failure to prevent security breaches costs the organisation huge losses and bad reputation. Technical solutions are abundant but nonetheless still unsuccessful to deter information security incidents. The root cause of incompliance is humans as they are the weakest link of security chain. This paper examines the information security control management particularly on information security awareness, training and education, risk analysis and management, information security policies and procedures as well as physical security monitoring, and cognitive factors which give impact towards the employees’ information security compliant behaviour in the organization. Based on convenient sampling, a survey was conducted to employees of public and private sectors in Malaysia who are the Software as a Service (SaaS) cloud users. Data was collected online and was analysed using PLS-SEM. Result shows that information security control management and cognitive factors have high significant impact in deterring information security misbehaviour in the context of cloud users.
MiNDesign: Toward a Modeling, Simulation and Evaluation Platform for Human Cognitive Performance

MiNDesign – an alternative way of human cognitive performance evaluation method is proposed and implemented in this paper. Firstly, the roadmap and whole framework of this solution are illustrated. Then, a cognitive architecture is setup as the basis of cognitive modeling for human behavior of this platform, and functions of each component and its key point are discussed. As an example, cognitive modeling and model-based cognitive performance evaluation for numeric keypad layout operating task are conducted. The results show that the way proposed in this paper is effective, objective and accurate than traditional rating scales and physiological measurement methods, and which has obvious advantages for human performance evaluation.
Design of Distribution Network Pre-planning System Based on GIS

Aiming at the demand of distribution network pre-planning, this paper analyses the current situation and existing problems of distribution network pre-planning, and proposes a distribution grid pre-planning system based on GIS. Based on the design concept and technical route of the grid planning system in the early stage of distribution network, the functional modules and overall architecture of the system are designed. Developing system design based on GIS, maximizing the integration of grid information, providing more accurate and diversified decision-making basis for distribution network planning business personnel, and improving the quality and accuracy of distribution network pre-planning.
A Novel Approach for Credit Card Fraud Detection Through Deep Learning

In the current scenario of the world, digitalization looks expanding ubiquity as a result of consistent, helpful and advantageous utilization of e-commerce. It has demonstrated to be a slam gasp and simple method of installment passage. Purchasers pick online installment and e-shopping, due to time comfort. Because of this, an enormous measure of online business use, there is an immense addition in credit card frauds moreover. Fraudsters attempt to abuse the card and straightforwardness of online installments. To beat the fraudster’s action become fundamental, the primary point is to verify credit card transactions; in this way, individuals can utilize e-exchanges securely and effectively. To identify the charge card misrepresentation, there are different methods which depend on deep learning, logistic regression, naïve Bayesian, support vector machine (SVM), neural network, artificial immune system, nearest neighbor, data mining, decision tree, fuzzy logic-based system, genetic algorithm and so on.
Classification vs Deep Learning in Cancer Degree on Limited Histopathology Datasets

Today deep learning techniques (DL) are the main focus in classification of disease conditions from histology slides, but this task used to be done by more traditional machine learning pipeline algorithms (MLp). The first can learn autonomously, without any feature engineering. But some questions arise: can we design a fully automated MLp? Can that MLp match DL, at least in some tasks? how should it be designed? Can both be useful and/or complement each other? In this chapter we try to answer those questions. In the process, we design an automated MLp, build DL architectures, apply both to cancer grading, compare accuracy experimentally and discuss the remaining issues. Surprisingly, a carefully designed MLp procedure (acc. 86.5%) compared favorably to deep learning (best acc. 82%) and to humans (acc. 84%) when detecting degree of atypia for breast cancer prognosis on limited-sized publicly available Mytos dataset, with the same DL architectures that achieved accuracies of 97% on a different cancer classification task. Most importantly, we discuss advantages and limitations of alternatives, in particular what features make DL superior and may justify that choice, but also how MLp can be almost fully automated and produce useful structures characterization. Finally, we raise challenges, identifying how MLp and DL should evolve to offer explainability and integrate humans in the loop.
Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices

The paper introduces the Secure kNN (SkNN) approach to data classification and querying. The approach is founded on the concept of Secure Chain Distance Matrices (SCDMs) whereby the classification and querying is entirely delegated to a third party data miner without sharing either the original dataset or individual queries. Privacy is maintained using two property preserving encryption schemes, a homomorphic encryption scheme and bespoke order preserving encryption scheme. The proposed solution provides advantages of: (i) preserving the data privacy of the parties involved, (ii) preserving the confidentiality of the data owner encryption key, (iii) hiding the query resolution process and (iv) providing for scalability with respect to alternative data mining algorithms and alternative collaborative data mining scenarios. The results indicate that the proposed solution is both efficient and effective whilst at the same time being secure against potential attack.
Technology Transformation in Education: Consequences of Digitalization

In the article, the authors highlight current trends that are changing the landscape of the educational process and teaching technologies, as well as related methodological tasks and challenges that the teaching community faces. The active pedagogical and digital technologies considered in this contribution are presented as a joint tool of the educational environment, which makes it possible to implement principles of continuous education, build individual educational routes, and organize mixed learning. The issue of building an infrastructure related to the informatization at the same time opens up opportunities for the transition to a new level of education, and the application of active methods is aimed at training professionals who are guaranteed demand in the labor market in terms of their professional competences, and who easily use mobile and Internet technology that simultaneously develops their cultural competences.
Медиатизация культуры как фактор современной социодинамики

The modern stage of development of society is characterized by the emergence of a new type of modern sociodynamics such as the stage of mediatization of culture and society, which is the result of the emergence of modern information technologies. One of these new technologies is the Multimedia system, which led to the formation of a new type of communication, as well as a new cultural and information space.
An Incentive Analysis of Some Bitcoin Fee Designs (Invited Talk)

In the Bitcoin system, miners are incentivized to join the system and validate transactions through fees paid by the users. A simple pay your bid auction has been employed to determine the transaction fees. Recently, Lavi, Sattath and Zohar [Lavi et al., 2019] proposed an alternative fee design, called the monopolistic price (MP) mechanism, aimed at improving the revenue for the miners. Although MP is not strictly incentive compatible (IC), they studied how close to IC the mechanism is for iid distributions, and conjectured that it is nearly IC asymptotically based on extensive simulations and some analysis. In this paper, we prove that the MP mechanism is nearly incentive compatible for any iid distribution as the number of users grows large. This holds true with respect to other attacks such as splitting bids. We also prove a conjecture in [Lavi et al., 2019] that MP dominates the RSOP auction in revenue (originally defined in [Goldberg et al., 2006] for digital goods). These results lend support to MP as a Bitcoin fee design candidate. Additionally, we explore some possible intrinsic correlations between incentive compatibility and revenue in general.