Reviewerly/exHarmony huggingface dataset:
A comparative study of correlation coefficients used to assess the solar and wind complementarity in Mexico

Energetic complementarity maps can be used to find sites with good potential for the combined electricity generation of two natural resources with high variability. This chapter reviews and compares the main correlation coefficients used to quantify the energetic complementarity between solar and wind resources in a specific region. The correlation metrics presented are the Pearson, Kendall, and Spearman correlation coefficients, as well as the canonical correlation analysis and the cross-correlation. These statistical metrics are applied to obtain the annual energetic complementarity map of Mexico. The results obtained using the different metrics are compared, highlighting the differences in the maps obtained, and the advantages and disadvantages of each metric. Also, in order to evaluate the accuracy of the metrics used, a comparison between the maps obtained and zones with photovoltaic and wind power plants already installed is presented. The results obtained show that it is recommended to use the Pearson correlation coefficient in this type of application because of its simplicity and precision.
A Chaotic Image Encryption Algorithm Based on Sub-block Spiral Scans and Matrix Multiplication

With the development of information technology, image secrecy technology has gradually received widespread attention. A chaotic image encryption scheme based on the mixture of sub-block spiral scans transform and matrix multiplication is proposed. Firstly, an increased sub-block segmentation spiral scanning method is presented, which enhances the effect of confusion during encryption. Secondly, a novel method of constructing the multiplying matrix of left and right has been utilized, which greatly reduces the encryption time while ensuring the encryption effect. Finally, the simulation and the analysis illustrate that the proposed algorithm has a greater cryptography effect and better key safety, while it can also effectively withstand exhaustive attacks, statistical attacks, and differential attacks.
Digging Deeper into the State of the Practice for Domain Specific Research Software

To improve the productivity of research software developers we need to first understand their development practices. Previous studies on this topic have collected data by surveying as many developers as possible, across a broad range of application domains. We propose to dig deeper into the state of the practice by instead looking at what developers in specific domains create, as evidenced by the contents of their repositories. Our methodology prescribes the following steps: i) Identify the domain; ii) Identify a list of candidate software; iii) Filter the list to a length of about 30 packages; iv) Collect repository related data on each package, like number of stars, number of open issues, number of lines of code; v) Fill in the measurement template (the template consists of 108 questions to assess 9 qualities (including the qualities of installability, usability and visibility)); vi) Rank the software using the Analytic Hierarchy Process (AHP); vii) Interview developers (the interview consists of 20 questions and takes about an hour); and, viii) Conduct a domain analysis. The collected data is analyzed by: i) comparing the ranking by best practices against the ranking by popularity; ii) comparing artifacts, tools and processes to current research software development guidelines; and, iii) exploring pain points. We estimate the time to complete an assessment at 173 person hours. The method is illustrated via the example of Lattice Boltzmann Solvers, where we find that the top packages engaged in most of recommended best practices, but still show room for improvement with respect to providing API documentation, a roadmap, a code of conduct, programming style guide and continuous integration.
MRVAE: Variational Autoencoder with Multiple Relationships for Collaborative Filtering

Variational Autoencoder (VAE)-based collaborative filtering (VAE-based CF) methods have shown their effectiveness in top-N recommendation. Mult-VAE is one of them that achieves state-of-the-art performance. Multinomial likelihood and additional hyperparameter $$\beta $$ on the KL divergence term controlling the strength of regularization make Mult-VAE a strong baseline. However, Mult-VAE uses non-linear MLPs as its encoder and decoder, which will boost the performance on the dense datasets but degrade the performance on the sparse datasets in our experiments. While recent studies shed light on the non-linearity for modeling the relationships between users and items, they ignore the importance of linearity between users and items, especially on the sparse datasets. To bridge the gap and consider both the linearity and non-linearity user-item relationships, we design a hybrid encoder that incorporates both linearity and non-linearity, and use a linear decoder for VAE-based CF, which can achieve competitive performance on both sparse and dense datasets. Moreover, most VAE-based CF methods only consider the relationships between users and items but ignore the relationships between items for improving the performance in collaborative filtering. To overcome this limitation, we try to incorporate item-item relationships into VAE-based CF with the help of cosine similarity between items. Unifying these relationships into VAE-based CF forms our proposed method, Variational Autoencoder with Multiple Relationships (MRVAE) for collaborative filtering. Extensive experiments on several dense and sparse datasets show the effectiveness of MRVAE.
Simple Approach for Violence Detection in Real-Time Videos Using Pose Estimation With Azimuthal Displacement and Centroid Distance as Features

Detecting violence in real time videos is not an easy task even for the most advanced deep learning architectures, considering the subtle details of human behavior that differentiate an ordinary from a violent action. Even with the advances of deep learning, human activity recognition(HAR) in videos can only be achieved at a huge computational cost, most of the time also requiring special hardware for reaching an acceptable accuracy. We present in this paper a novice method for violence detection, a sub-area of HAR, which outperforms in speed and accuracy the state of the art methods. Our method is based on features extracted from the Pose estimator method OpenPose. These features are then transformed into more representative elements in the context of violence detection, which are then submitted to a LSTM neural network to learn how to identify violence. This work was inspired by the violencedetector.org, the first open source project for violence detection in real time videos.
EXPLORING CLOUD COMPUTING: Benefits, Threats and Mitigation Strategies

Cloud Computing is a booming technology that has immense potential and has proven to be a boon for education, small-scale businesses, financial, healthcare, and many other industries in recent years. The true potential of cloud computing was brought to light during the pandemic period as the education sector heavily relied upon the services provided by cloud service providers (CSP). Even though it has such potential, it also has many limitations and a vast area of it has not been discovered yet.If these limitations are minimized, cloud computing would prove to be helpful for many industries. In this paper will discuss the cloud NIST architecture, benefits, limitations, and how to overcome those limitations in detail.
Blockchain Technology in Apparel Supply Chains

The apparel industry is one of the fastest-growing industry employing millions of workers across the world. The majority of these people work in appalling working conditions resulting in the industry being called an industry with ‘slave labour’. Moreover, the industry is known to be the second-largest polluter while consuming a significant amount of natural resources. Literature suggests that unsustainable practices are the result of a lack of visibility in the supply chain. Thus, promoting visibility in supply chains has become imperative in addressing the sustainability issues in the apparel industry. This book chapter presents how blockchain technology addresses the transparency issue and aid in the development of the sustainable apparel industry. The immutability and decentralisation properties of blockchain technology can promote sustainability. Benefits offered by blockchain technology implementation may vary among the supply chain members. For example, the use of blockchain creates ledgers of information that ensure the integrity and authenticity of organic cotton. Meanwhile, at manufacturers, the use of blockchain facilitates designers and merchandisers to share accurate information and collaborate remotely that assists to react faster to market trends. Moreover, it is used to monitor factory safety in its global supply chains. Despite the benefits offered by blockchain technology when implemented in supply chains, there are several challenges related to the implementation. In this chapter technological, organisational, and environmental (TOE) framework is used to examine the blockchain implementation challenges. Issues with compatibility, lack of skilled expertise, lack of clear government regulations, and privacy issues are some of the major challenges of implementing blockchain technology.
Medical Image Demosaicing Based Design of Newton Gregory Interpolation Algorithm

In this paper, Field-Programmable Gate Array (FPGA) implementation-based image demosaicing is carried out. The Newton Gregory interpolation algorithm is designed based on FPGA frame work. Interpolation is the method of assessing the value of a function for any in-between value of self-regulating variable, whereas the method of computing the value of the function outside the specified range is named extrapolation. The natural images are collected from Kodak image database and medical images are collected from UPOL (University of Phoenix Online) database. The proposed algorithm is executed on using Xilinx ISE (Integrated Synthesis Environment) Design Suite 14.2 and is confirmed on Xilinx Virtex-5 FPGA board. The main aim of this paper is to develop a demosaicing method based on the FPGA technique. Implementing image processing on hardware reduces the cost and make simpler for debugging and verification. The hardware implementation in this system gives the better quality of output image. Implementing image processing on hardware reduces the cost and makes simpler for debugging and verification. Newton- Gregory interpolation technique produces the full resolution of green channel with red and blue channels. The experimental result clearly shows that proposed Newton-Gregory interpolation-based image demosaicing outperformed other algorithms. FPGA implementation attains the maximum Peak Signal-To-Noise Ratio (PSNR) of 40.53 dB and MATLAB (Matrix Laboratory<b>)</b>. implementation attains the maximum PSNR of 38.15 dB.
Further Lessons from the JML Project

JML is an ambitious project in formal specification and verification that has aimed to bring value to Java programmers. As an international, cooperative effort, JML has been a uniting force, bringing together tools such as KeY, OpenJML, and many others. Specification language designers can learn much from the experience gathered by the JML team. The lessons of the JML project can be useful to others designing specification languages and tools, not only for object-oriented languages such as Java, but more generally.
Covert Authentication from Lattices

Introduced by von Ahn et al. (STOC’05), covert two-party computation is an appealing cryptographic primitive that allows Alice and Bob to securely evaluate a function on their secret inputs in a steganographic manner, i.e., even the existence of a computation is oblivious to each party - unless the output of the function is favourable to both. A prominent form of covert computation is covert authentication, where Alice and Bob want to authenticate each other based on their credentials, in a way such that the party who does not hold the appropriate credentials cannot pass the authentication and is even unable to distinguish a protocol instance from random noise. Jarecki (PKC’14) put forward a blueprint for designing covert authentication protocols, relying on a covert conditional key-encapsulation mechanism, an identity escrow scheme, a covert commitment scheme and a $$\varSigma $$ -protocol satisfying several specific properties. He also proposed an instantiation based on the Strong RSA, the Decisional Quadratic Residuosity and the Decisional Diffie-Hellman assumptions. Despite being very efficient, Jarecki’s construction is vulnerable against quantum adversaries. In fact, designing covert authentication protocols from post-quantum assumptions remains an open problem. In this work, we present several contributions to the study of covert authentication protocols. First, we identify several technical obstacles in realizing Jarecki’s blueprint under lattice assumptions. To remedy, we then provide a new generic construction of covert Mutual Authentication (MA) protocol, that departs from given blueprint and that requires somewhat weaker properties regarding the employed cryptographic ingredients. Next, we instantiate our generic construction based on commonly used lattice assumptions. The protocol is proven secure in the random oracle model, assuming the hardness of the Module Learning With Errors (M-LWE) and Module Short Integer Solution (M-SIS) and the NTRU problems, and hence, is potentially quantum-safe. In the process, we also develop an approximate smooth projective hashing function associated with a covert commitment, based on the M-LWE assumption. We then demonstrate that this new ingredient can be smoothly combined with existing lattice-based techniques to yield a secure covert MA scheme.
Reviews Based Sentiment Analysis for Optimizing Product Rating System

What others think is always important information in a decision-making process. In this new era, a majority of our time is spent on e-commerce websites, surfing through a wide variety of products. Due to a shortage of time and for the sake of better quality products a large fraction of todayâ€™s population relies on the rating and reviews of the products to buy them. The purpose of this paper is to build a chrome extension that generates an optimized product rating as an improvement over the traditional product rating system. The key point is to differentiate the reviews that affect the determining factor of a product from the futile ones. Keeping in mind the increasing trend of using emojis in review writing, this research work designed a process to examine emojis for assessing the sentiments of the review. To give users a convenient experience, the system has a feature to record the reviews with a microphone in addition to typing them into the comment box. The product uses a Rule-based approach to optimize the current rating system. This was achieved by an attempt to analyze the sentiments of valuable reviews and assign them individual scores, before further processing them to generate the final optimized rating.
An Adaptive Networking Solution Strategy Based on Real-Time Relative Positioning of Linkage Targets

Aiming at the problem of unstable formation status due to communication delays or other factors during the operation of multiple moving target formations. This paper proposes an adaptive network solution strategy based on real-time relative positioning of linked targets. The target role is dynamically generated through the formation autonomous space state confirmation technology, and the corresponding tasks are performed. The formation’s anti-destroy ability is improved. At the same time, the time baseline processing scheme is adopted. To ensure that the system can not only work normally when the data is delayed and temporarily interrupted, but also can ensure the accuracy of the benchmark. Finally, dynamic experiments show that the adaptive network solution strategy based on real-time relative positioning of linked targets is reliable and effective in practical applications, and can reach centimeter-level positioning accuracy.
Cost-Sensitive Active Learning for Incomplete Data

Practical data often suffer from missing attribute values and lack of class labels. A reasonable machine learning scenario involves obtaining certain values and labels at cost on request. In this article, we propose the cost-sensitive active learning through unified evaluation and dynamic selection (CALS) algorithm to handle the learning task in this new scenario. For data representation, we consider misclassification cost, label query cost, and attribute query cost. For the cost/benefit estimation, we design a unified assessment of attribute values and labels with softmax regression. For the selection of attribute value and label, we propose an optimal acquisition scheme with permutation and greedy strategies. We perform experiments with synthetic, benchmark, and domain datasets. The results of the significance test verify the effectiveness of CALS and its superiority over cost-sensitive active learning and missing data imputation algorithms.
Graph Convolutional Network With Unknown Class Number

The graph convolutional network (GCN), as a powerful tool in graph data processing, is widely exploited in many machine learning and computer vision tasks. However, existing GCNs usually assume that the network has fixed outputs, which is usually contrary to the real-world class number being unknown and incremental, leading to an open set classification problem in which the finite training dataset cannot contain all labels in the infinite testing data. To overcome these issues, a novel Bayesian model is proposed, in which we couple GCN and a deep generative clustering model in a unified framework. In our model, the GCN model is used to detect the known classes, the deep generative clustering model is designed to generate the novel classes, and a two-level label generative process is constructed to extend the finite GCN outputs to infinity and fuse the label generated by the GCN model and the deep generative model. Although posterior inference is difficult, our model leads to an efficient variational inference-based optimization method. Experiments on various datasets validate our theoretical analysis and demonstrate that our model can achieve state-of-the-art performance. Our source code has been released on the website.
3-D Acoustic Image Denoising for a Sonar System With Sparse Planar Arrays

The development of three-dimensional (3-D) underwater imaging systems is restricted by the high hardware costs associated with the use of a large number of transducers, as well as poor image quality due to degrading noise. This report presents the design of a low-complexity 3-D underwater imaging system that can generate high-quality images. In particular, a complex-weight sparse synthesis method for a large planar array is presented to reduce the number of active elements. Then, a denoising network referred to as an asymmetric-pyramid globe residual network is proposed to enhance the acoustic images generated by the sparse planar arrays. This method can perceive the entirety of the 3-D acoustic images for denoising and effectively reduce the degradation effects due to speckle noise and sidelobes. The simulation results demonstrate that the resulting image quality is higher than that achieved using previously developed networks in terms of the peak signal-to-noise ratio and structural similarity index. To validate the proposed system further, an actual system based on the proposed methods was devised and tested via lake-based trials. The experimental results demonstrate the notable improvements obtained considering the sparsity rate and image quality relative to those presented in previous literature.
Variable Structure Fuzzy Controller for Unstable Processes

A variable structure fuzzy controller (VSFC) is proposed for linear/nonlinear minimum phase unstable processes. A sliding surface is designed and further utilized as inputs to the fuzzy controller. The input-plane of the fuzzy controller is partitioned into sub-planes. This enhances the performance of the fuzzy controller as the state-space is sculpted in a number of regions. Genetic algorithm (GA)-based tuning approach is applied to obtain optimal parameters of VSFC. The performance of VSFC is evaluated on some unstable and nonlinear processes, including continuous stirred tank reactor (CSTR), to show its practical feasibility.
Speak-Correct: A Computerized Interface for the Analysis of Mispronounced Errors

Any natural language may have dozens of accents.Even though the equivalent phonemic formation of the word, if it is properly called in different accents, humans do have audio signals that are distinct from one another.Among the most common issues with speech, the processing is discrepancies in pronunciation, accent, and enunciation.This research study examines the issues of detecting, fixing, and summarising accent defects of average Arabic individuals in English-speaking speech.The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues.The proposed SpeakCorrect computerized interface employs a cuttingedge speech recognition system and analyses pronunciation errors with a speech decoder.As a result, some of the most essential types of changes in pronunciation that are significant for speech recognition are performed, and accent defects defining such differences are presented.Consequently, the suggested technique increases the Speaker's accuracy.SpeakCorrect uses 100 h of phonetically prepared individuals to construct a pronunciation instruction repository.These prerecorded sets are used to train Hidden Markov Models (HMM) as well as weighted graph systems.Their speeches are quite clear and might be considered natural.The proposed interface is optimized for use with an integrated phonetic pronounced dataset, as well as for analyzing and identifying speech faults in Saudi and Egyptian dialects.The proposed interface detects, analyses, and assists English learners in correcting utterance faults, overcoming problems, and improving their pronunciations.
Google Cloud Platform: Cloud Storage

Google Cloud Platform (GCP) offers Cloud Storage, a popular cloud computing resource for data storage. This note provides information about uploading data to Cloud Storage, creating BigQuery tables from data found in Cloud Storage, previewing files in Cloud Storage, and sharing Cloud Storage buckets.
Double-Layer Controller for Detecting Learners’ Erroneous Knowledge in Database Programming

Adaptive learning systems employ educational techniques and use computer algorithms to orchestrate the interaction with the learner. One example of their activities is the error detection and diagnosis. Error diagnosis serves for identifying the learners’ mistakes, their nature and the reason for happening. This module is important since it can help learners advance their knowledge. In view of this compelling need, this paper presents a double-layer controller for detecting learners’ erroneous knowledge in the tutoring of database programming, and specifically the Structured Query Language. The controller can reason about two main error categories, i.e. syntax and logic errors, and holds syntax and logical check operators. For the error diagnosis process, the syntax check operator incorporates a string-matching similarity technique, while the logical check operator incorporates the Start End Mid algorithm and the Sørensen–Dice coefficient. The educational software for the database programming, that incorporates the double-layer controller, was evaluated and the results showed that the presented mechanism has a significant effect on learners’ performance.