Reviewerly/exHarmony huggingface dataset:
Machine Learning based Efficient Recommendation System for Book Selection using User based Collaborative Filtering Algorithm

Recommender system is a new generation of internet tool that helps users to access the web and receive information about their preferences. Using an online recommender is comparatively an easy and faster procedure to purchase items and this is done quickly. Recommendation systems plays an indispensable role in ecommerce websites to help users in identifying the right goods. One of the best methods to increase profits and attract customers is a recommendation process. The existing methodologies allow the systems to collect the irrelevant data and lead to a downfall in attracting the users and completing their work in a quick and reliable way. This paper provides an overview of the Recommendation Systems that is currently employed in the operations of the online book shopping domain. This paper proposes a simple understandable system for book recommendations that help readers to suggest the right book, which is to be studied next. In recent years, information analysis challenge has been focused on for the administration recommendation system. For clients, network assets are completely linked and quickly developed. The proposed method works on training, feedback, management, reporting, configuration, and using it to offer useful information to the user in order to aid in decision-making and data item recommendations. We have used a User Based Collaborative Filtering (UBCF) approach and measured the performance of similarity measures in recommending books to a user. The proposed system’s overall architecture is introduced and its implementation is represented with a model design.
Impact of financial development on economic growth: Evidence from<scp>Sub‐Saharan</scp>Africa

Abstract Many African countries have experienced episodes of weak growth since 1970 through the 2000s. A fundamental intermediation gap in Sub‐Saharan Africa (SSA) is the extremely limited financing for medium to long term lending facilities to promote growth. Thus, given the importance of financial development on economic growth, this study investigates the impact of financial development on economic growth in SSA, which is sub‐divided into low‐, middle‐ and upper‐income groupings to ascertain whether differences in income levels across countries affect the relative impact of finance on growth. The study adopts the dynamic and static panel data model to analyse 30 SSA countries using annual data over the 1985–2015 period. The findings indicate that financial depth and financial intermediation reduce per capita income growth in low‐ and middle‐income countries. However, it increases growth in upper‐income and the overall sample of SSA countries. Credit supply positively impacts growth in low‐income countries but exerts a significantly negative impact on growth in middle‐income and the overall sample of SSA countries. Financial liberalisation promotes growth in upper‐income and the overall SSA. However, it reduces growth in low‐ and middle‐income countries. Financial development and financial liberalisation are important factors that affect economic growth given different income levels.
The Essential Role of Technology in the Public Health Battle Against COVID-19

Technology has played an important role in responding to the novel coronavirus (SARS-CoV-2) and subsequent COVID-19 pandemic. The virus's blend of lethality and transmissibility have challenged officials and exposed critical limitations of the traditional public health apparatus. However, throughout this pandemic, technology has answered the call for a new form of public health that illustrates opportunities for enhanced agility, scale, and responsiveness. The authors share the Microsoft perspective and illustrate how technology has helped transform the public health landscape with new and refined capabilities – the efficacy and impact of which will be determined by history. Technologies like chatbot and virtualized patient care offer a mechanism to triage and distribute care at scale. Artificial intelligence and high-performance computing have accelerated research into understanding the virus and developing targeted therapeutics to treat infection and prevent transmission. New mobile contact tracing protocols that preserve patient privacy and civil liberties were developed in response to public concerns, creating new opportunities for privacy-sensitive technologies that aid efforts to prevent and control outbreaks. While much progress is still needed, the COVID-19 pandemic has highlighted technology's importance to public health security and pandemic preparedness. Future multi-stakeholder collaborations, including those with technology organizations, are needed to facilitate progress in overcoming the current pandemic, setting the stage for improved pandemic preparedness in the future. As lessons are assessed from the current pandemic, public officials should consider technology's role and continue to seek opportunities to supplement and improve on traditional approaches.
High-Speed Area-Efficient VLSI Architecture of Three-Operand Binary Adder

Three-operand binary adder is the basic functional unit to perform the modular arithmetic in various cryptography and pseudorandom bit generator (PRBG) algorithms. Carry-save adder (CS3A) is the widely used technique to perform the three-operand addition. However, the ripple-carry stage in the CS3A leads to a high propagation delay of O(n). Moreover, a parallel prefix two-operand adder such as Han-Carlson (HCA) can also be used for three-operand addition that significantly reduces the critical path delay at the cost of additional hardware. Hence, a new high-speed and area-efficient adder architecture is proposed using pre-compute bitwise addition followed by carry-prefix computation logic to perform the three-operand binary addition that consumes substantially less area, low power and drastically reduces the adder delay to O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> n). The proposed architecture is implemented on the FPGA device for functional validation and also synthesized with the commercially available 32nm CMOS technology library. The post-synthesis results of the proposed adder reported 3.12, 5.31 and 9.28 times faster than the CS3A for 32-, 64- and 128- bit architecture respectively. Moreover, it has a lesser area, lower power dissipation and smaller delay than the HC3A adder. Also, the proposed adder achieves the lowest ADP and PDP than the existing three-operand adder techniques.
A Four-Qubits Code that is a Quantum Deletion Error-Correcting Code with the Optimal Length

This paper provides a new instance of quantum deletion error-correcting codes. This code can correct any single quantum deletion error, while our code is only of length 4. This paper also provides an example of an encoding quantum circuit and decoding quantum circuits. It is also proven that the length of any single deletion error-correcting codes is greater than or equal to 4. In other words, our code is optimal for the code length.
Learning Diverse Fashion Collocation by Neural Graph Filtering

Fashion recommendation systems are highly desired by customers to find visually-collocated fashion items, such as clothes, shoes, bags, etc. While existing methods demonstrate promising results, they remain lacking in flexibility and diversity, e.g. assuming a fixed number of items or favoring safe but boring recommendations. In this paper, we propose a novel fashion collocation framework, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neural Graph Filtering</b> , that models a flexible set of fashion items via a graph neural network. Specifically, we consider the visual embeddings of each garment as a node in the graph, and describe the inter-garment relationship as the edge between nodes. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We further include a style classifier augmented with focal loss to enable the collocation of significantly diverse styles, which are inherently imbalanced in the training set. To facilitate a comprehensive study on diverse fashion collocation, we reorganize Amazon Fashion dataset with carefully designed evaluation protocols. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset. Extensive experimental results show that our approach significantly outperforms the state-of-the-art methods with over 10% improvements on the standard AUC metric. More importantly, 82.5% of the users prefer our diverse-style recommendations over other alternatives in a real-world perception study.
Shoulder surfing experiments: A systematic literature review

In search of the silver bullet to solve the password problem, the field of knowledge-based authentication has become bloated with novel proposals aiming to replace textual passwords. The emphasis on the quantity of studies as opposed to the quality of evaluation has made it difficult to compare the methods, as well as to validate and generalize the results. To improve the quality of security and usability evaluations, experimental design decisions should be reviewed and standardized. In this systematic review, we focus on the evaluation of the shoulder surfing attack (SSA) vulnerability. We formulate two research questions to help us determine how the design of the method should affect the SSA experimental design process, and how different design decisions affect the validity and interpretability of the results under various assumptions and threat models. To provide the researchers with comprehensive literature on SSA evaluation, we identify empirical shoulder surfing studies conforming to a predefined set of quality criteria. Based on the design features extracted from the experiments, we develop an evaluation framework for the assessment of the shoulder surfing experimental setup. In the follow-up analysis, we assess the proposed methods’ design features, and the quality of their SSA experiments, using Schaub et al.’s design aspect and our SSA evaluation frameworks, respectively. Through exhaustive analysis, we strive to streamline and standardize experimental decisions by showcasing their impact on the outcome of the study, and generate guidelines for a more objective design of shoulder surfing experiments.
Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers

Data-driven classification algorithms have proven highly effective in a range of complex tasks. However, their output is sometimes questioned, as the reasoning behind it may remain unclear due to a high number of poorly interpretable parameters used during training. Evidence-based (factual) explanations for single classifications answer the question why a particular class is selected in terms of the given observations. On the contrary, counterfactual explanations pay attention to why the rest of classes are not selected. Accordingly, we hypothesize that providing classifiers with a combination of both factual and counterfactual explanations is likely to make them more trustworthy. In order to investigate how such explanations can be produced, we introduce a new method to generate factual and counterfactual explanations for the output of pretrained decision trees and fuzzy rule-based classifiers. Experimental results show that unification of factual and counterfactual explanations under the paradigm of fuzzy inference systems proves promising for explaining the reasoning of classification algorithms.
Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method

Accurate prediction of the photovoltaic (PV) power generation is of great significance for the efficient management of the power grid. In order to strengthen the interpretability of the data-driven models for PV power prediction and to further improve the forecasting accuracy, a novel double-input-rule-modules (DIRMs) stacked deep fuzzy model (DIRM-DFM) is proposed in this study. Firstly, the proposed stacked structure of DIRM-DFM is presented. This novel modular structure adopts a bottom-up, layer-by-layer design scheme by stacking the DIRMs which has only two input variables. This scheme assures the interpretability of the proposed novel fuzzy model. Then, to guarantee the performance of DIRM-DFM, its learning mechanism, including the training data generation, the construction of the DIRMs, are given in detail. This learning mechanism has fast learning speed and excellent approximation ability, because each DIRM is optimized by the popular least square method. Finally, two real-world experiments for predicting the PV power generation are conducted to verify the proposed DIRM-DFM, and detailed comparisons are made with traditional and deep fuzzy models, shallow and deep neural networks. Experimental results clearly demonstrated that the proposed DIRM-DFM has the best accuracy and the reactively fast training speed while having the apparent advantages of interpretability.
Developing image analysis pipelines of whole-slide images: Pre- and post-processing

Abstract Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size – typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.
Image restoration using overlapping group sparsity on hyper-Laplacian prior of image gradient

Due to the ill-posed nature of image restoration, seeking a meaningful image prior is still a great challenge in the field of image processing. The total variation with overlapping group sparsity (OGS-TV) has been successfully applied for image denoising/deblurring. In this paper, we further study the overlapping group sparsity of the image gradient. The sparsity is measured by the ℓq quasi-norm (0<q<1). The proposed regularizer comes down to the well-known hyper-Laplacian prior if the overlapping group size is 1. Although it seems to be a simple extensive study compared with the previous works, its regularization capability and corresponding mathematical problems are still in demand for imaging science. To solve the non-convex and non-smooth minimization problem, we use the alternating direction method of multipliers as the main algorithm framework. The difficult inner subproblem is tackled by the majorization-minimization method with the sophisticatedly derived majorizer. We carry out some numerical experiments to demonstrate the effectiveness of the proposed regularizer in terms of PSNR and SSIM values.
An improved method for direct incident solar radiation calculation from hourly solar insolation data in building energy simulation

Solar radiation considerably influences the energy consumption of buildings and the power production of building integrated photovoltaic (BIPV) systems. Hourly solar insolation (Wh/m2), represented as the amount of solar irradiance collected on the ground during a 1-h period, is the most common solar radiation data available and widely used in weather files applied in building energy modeling programs (BEMPs). Because the solar beam and position vary over time, the use of hourly insolation data as the input might result in errors in the estimation of the direct incident solar radiation on a particular surface. In this study, methods used in BEMPs for direct incident solar radiation calculations are first analyzed, and an improved method adopting a new algorithm for estimating the solar irradiance is proposed. The algorithm assumes that the solar irradiance changes linearly within a 1-h period and can be estimated based on the solar irradiance at the half clock and slope. The collected direct normal solar irradiance data of 2016 from eight solar radiation stations in China were used to demonstrate the proposed method and evaluate its performance by comparing the results with those from three conventional methods used in BEMPs along with the ground truth measurements. In addition, in this study, factors affecting the accuracy of the calculation results are explored. The results of the estimated direct incident solar radiation show that the proposed method achieves the best accuracy, followed by the methods used in DOE-2, EnergyPlus, and DeST. The proposed method guarantees that the hourly direct solar insolation will remain the same and reflects the variation in the direct solar irradiance across a 1-h time frame. The proposed method can be adopted in BEMPs to improve the accuracy of the solar radiation calculation, thereby improving the accuracy of the simulated building performance and the BIPV production.
BTCAS: A Blockchain-Based Thoroughly Cross-Domain Authentication Scheme

In many real resource access scenarios, the parties who require to establish communication may be unable to effectively identify and verify some authentication messages of the other party due to the use of completely different cryptography settings, making it difficult to authenticate each other in such scenarios. We usually define the above-mentioned problem as “cross-domain authentication”. Although many research works have been devoted to solving the problem of entity authentication for cross-domain communication, the problem of “incomplete cross-domain” widely exists in existing works. Existing solutions based on some common underlying cryptography foundations greatly limit the application of such cross-domain authentication schemes. In order to solve the problem of “incomplete cross-domain”, this paper proposes a thoroughly cross-domain authentication scheme based on blockchain technology, which can be used by participants from different domains that adopt totally different settings. Our security analysis demonstrates that the scheme is provably secure in the standard model and the experimental results show the efficiency of our scheme.
Transfer Learning and Fine Tuning in Mammogram BI-RADS Classification

The BI-RADS report system is widely used by radiologists and clinicians to document relevant findings in the mammogram exam by using a 6 category final assessment. Deep learning has achieved a high level of accuracy in multi category classification of natural images. Because of that, it is of interest to address the mammography malignancy classification according to the established BI-RADS categories. In this work, we use transfer learning on NASNet Mobile and fine tuning on VGG16 and VGG19 to classify mammogram images according to the BI-RADS scale on the INbreast dataset. Our proposed methodology achieved an accuracy (ACC) of 90.9% and a macro averaged area under the receiver operating characteristic curve (AUC) of 99.0%; outperforming some of the similar works found in the literature review.
3D Dynamic Hand Gestures Recognition Using the Leap Motion Sensor and Convolutional Neural Networks

Defining methods for the automatic understanding of gestures is of paramount importance in many application contexts and in Virtual Reality applications for creating more natural and easy-to-use human-computer interaction methods. In this paper, we present a method for the recognition of a set of non-static gestures acquired through the Leap Motion sensor. The acquired gesture information is converted in color images, where the variation of hand joint positions during the gesture are projected on a plane and temporal information is represented with color intensity of the projected points. The classification of the gestures is performed using a deep Convolutional Neural Network (CNN). A modified version of the popular ResNet-50 architecture is adopted, obtained by removing the last fully connected layer and adding a new layer with as many neurons as the considered gesture classes. The method has been successfully applied to the existing reference dataset and preliminary tests have already been performed for the real-time recognition of dynamic gestures performed by users.
What Research Has Revealed About Readers’ Struggles With Comprehension in the Digital Age: Moving Beyond the Phonics Versus Whole Language Debate

Abstract The purpose of this article is to look anew at the concepts of reading, reading development, and reading research as they exist beyond the boundaries of the “science of reading” controversy. The context for this reconceptualization is reading in the digital age and the challenges that today’s readers confront daily. Those challenges include information saturation, the proliferation of misleading and malicious online content, the struggle to use valid evidence to support claims, and the tendency to treat complex issues in an overly simplistic fashion. How these concerns pertain to the ongoing “science of reading” debate is also considered. The author concludes with an overview of three recommendations for addressing the challenges faced by readers in this digital age. If they are to be realized, these recommendations require the consolidated effort of reading researchers, reading educators, school leaders, parents, policymakers, and students themselves.
A multi-objective optimized replication using fuzzy based self-defense algorithm for cloud computing

Cloud computing has attracted increasing attention in data management. Data replication, which brings files closer to the data consumers, is a well-known technique that reduces access time and bandwidth consumption. This paper addresses two issues concerning replica placement process. The first is how to reduce access costs and replication costs that are two conflicting goals. To achieve this, we propose a multi-objective optimized placement algorithm based on meta-heuristic technique and fuzzy system that finds the optimal locations for replicas by balancing the trade-offs among the six optimization objectives (i.e., system availability, service time, load, energy consumption, latency, and centrality). The second issue is how to determine the optimal number of replicas since storing a great number of replicas in cloud is expensive. To solve this problem, we determine the number of replicas without excessively reducing the performance. In addition, we improve self-defense algorithm by a new prey-predator model based on a fuzzy system to simulate the interaction between prey and predator population. The superior results with ten benchmark functions demonstrate the merits of the proposed fuzzy-self-defense algorithm in solving the problems compared with seven optimization algorithms. Moreover, the extensive simulations with CloudSim prove that the proposed replication algorithm outperforms the main existing replication strategies in terms of hit ratio, number of replications, load variance, latency, average service time, availability, and energy consumption.