Reviewerly/exHarmony huggingface dataset:
Chaining Zscore and feature scaling methods to improve neural networks for classification

Neural networks for classification aim at identifying the class label of new observation based training data containing instances whose category memberships are known. Therefore the data fed into neural networks has to be preprocessed to enhance its quality resulting in promoting the extraction of meaningful insights of data. However, the fact of processing data until you have the required high quality is challenging and time-consuming to manually search for the best method in a sequence of preprocessing independent methods. For feature scaling methods, they consist of scaling the dataset into the same range of data without monitoring data outliers that should eventually occur in the data source. Zscore for outlier's detection suffers from the issue of predefining the parameters. This paper discussed various approaches that are applied to scale features and detect outliers during data pre-processing. Thereafter, the paper proposed the algorithm that combines Zscore as an outlier's detection method with every classical feature scaling method in high-dimensional data. The proposed algorithm has benefits in selecting the optimal subset of methods from a sequence of chained methods, detecting outliers, and removing zero variance predictors. The study findings from five sample sizes revealed that the proposed method significantly excels the classical method in terms of accuracy. The outstanding from them was performed at the rate of 99.67% and had a significant difference of 0.20% over classical feature scaling.
Causality-aware Enhanced Model for Multi-hop Question Answering over Knowledge Graphs

To improve the performance of knowledge graph-based question answering system (KGQA), several approaches have been developed to construct a semantic parser based on entity linking, relation identification and logical/numerical structure identification. However, existing methods arrive at answers only by maximizing the data likelihood only on the sparse or imbalanced explicit relations, ignoring the potentially large number of latent relations. It makes KGQA suffer from a high level of spurious entity relations and missing link challenge. In this paper, we propose a causal filter (CF) model for KGQA (CF-KGQA), which performs causal interference on the relation representation space to reduce the spurious relation representation in a data-driven manner, i.e., the goal of this work is to comprehensively discover disentangled latent factors to alleviate the spurious correlation problem in KGQA. The model comprises a causal pairwise aggregator (AP) and a disentangled latent factor aggregator (AC). The former filters out most spurious entity relations inconsistent to their dense groups’ neighborhood, and generates a causal pairwise matrix among all the candidate relations. The latter learns the latent relation representation via an encoder–decoder on the causal pairwise matrix. It disconnects the latent factor and the causal confounder beneath the knowledge embedding space by causal intervention. To prove the effectiveness and efficiency of the proposed approach, we test CF-KGQA and other state-of-the-art methods on four public real-world datasets. The experiments indicate that our approach outperforms the recent methods and is also less sensitive to the spurious correlation problem, thus demonstrating the robustness of CF-KGQA.
Quantum Bridge Analytics II: QUBO-Plus, network optimization and combinatorial chaining for asset exchange

Quantum Bridge Analytics relates to methods and systems for hybrid classical-quantum computing, and is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future. This is the second of a two-part tutorial that surveys key elements of Quantum Bridge Analytics and its applications. Part I focused on the Quadratic Unconstrained Binary Optimization (QUBO) model which is presently the most widely applied optimization model in the quantum computing area, and which unifies a rich variety of combinatorial optimization problems. Part II (the present paper) introduces the domain of QUBO-Plus models that enables a larger range of problems to be handled effectively. After illustrating the scope of these QUBO-Plus models with examples, we give special attention to an important instance of these models called the Asset Exchange Problem (AEP). Solutions to the AEP enable market players to identify exchanges of assets that benefit all participants. Such exchanges are generated by a combination of two optimization technologies for this class of QUBO-Plus models, one grounded in network optimization and one based on a new metaheuristic optimization approach called combinatorial chaining. This combination opens the door to expanding the links to quantum computing applications established by QUBO models through the Quantum Bridge Analytics perspective. We show how the modeling and solution capability for the AEP instance of QUBO-Plus models provides a framework for solving a broad range of problems arising in financial, industrial, scientific, and social settings.
Variational cold-start resistant recommendation

Conventionally, cold-start limitations are managed by leveraging side information such as social-trust relationships. However, the relationships between users in social networks are complex, uncertain, and sparse. Therefore, it is necessary to extract beneficial social connections to make the recommendation models cold-start resistant. Towards this end, we propose a novel recommendation model called Variational Cold-start Resistant Recommendation (CORE-VAE). More concretely, we employ a social-aware similarity function and a graph convolutional network (GCN) to generate robust social-aware user representations that account for the complexities, uncertainties, and sparse nature of the social-trust network. Subsequently, these powerful social-aware representations aid us in producing cold-start resistant rating vectors for all users. To explore the rich user rating information, we propose an expressive variational autoencoder (VAE) model. Unlike earlier VAE-based CF models, CORE-VAE utilizes a novel prior distribution and a well-designed skip-generative network to alleviate the posterior collapse issue considerably. Besides, CORE-VAE can also capture the latent space’s uncertainty and ensure that observations and their accompanying latent variables have high mutual information. Overall, these novel techniques dramatically help produce better latent representations for generating more accurate recommendations. We show that CORE-VAE outperforms numerous competitive baseline models on real-world datasets through comprehensive empirical evaluation and analysis.
Data Confidentiality in Healthcare Monitoring Systems Based on Image Steganography to Improve the Exchange of Patient Information Using the Internet of Things

Recently, with the availability of fast and reliable Internet, the distance between a patient and a doctor is becoming unimportant. Physicians will be able to request the medical images of their patients regardless of the geographical area. However, a lot of challenges face such successful implementation. To facilitate remote diagnosis, patient electronic medical record (EMR), including medical images, that originates in one system needs to be exchanged either within the same organization or across different organizations. Steganography is the practice of concealing a secret message inside a cover medium. In this paper, steganography will be used to embed the patient's personal information securely and imperceptibly in their medical images to enhance confidentiality in case of a distant diagnosis. The security of the medical data is improved to maintain confidentiality and integrity using IoT. The least significant bit of the approximate coefficient of integer wavelet transform is proposed. The distortion between the cover image and stego-image is obtained by measuring the mean square error and PSNR, and normalized cross-correlation is utilized to estimate the degree of closeness between the cover image and stego-image.
A decentralized autonomous personal data management system in banking sector

The present security systems encounter vulnerable surveillance and protection breaches in financial domains day by day. These systems compromise the privacy of the users in such a way that their personal data are collected and controlled by the third-parties. In this regard, Bitcoin has been implemented in the financial space. It has made possible the trusted and auditable computing via decentralized network associated with a public ledger. This work proposes a decentralized personal data management system for banking sector using block chain which makes sure that the user can control their personal data in their own .It presents a Privacy-Protected Blockchain network in which all data is encoded within a controlled time period. Despite the fact that the data is available in the past, this design can successfully preserve user privacy and protect from deceivers, providing the data more stable and healthier. Blockchain has its advantages over features such as transparency, decentralization and immutability. In the proposed model, a protocol is implemented which converts the blockchain into automated access control manager. There is no need for a third party to be trusted for the implementation of this model. In the proposed system, the transactions are not limited to financial transactions like Bitcoin, it also includes data storing, sharing and querying. Hence, the use of blockchain could reduce the overly concern about securing and compartmentalizing the data.
GraphSPARQL

In recent years, knowledge graphs have become widely adopted for storing and managing vast amounts of data, powering various applications. However, SPARQL as the query language for accessing those knowledge graphs has a steep learning curve and is too complex for many use cases. This paper presents GraphSPARQL, a middleware that allows accessing arbitrary SPARQL endpoints by using GraphQL, supporting the GraphQL operations query and mutation. GraphSPARQL abstracts the complexity of SPARQL without losing the ability to address classes and properties of distinct ontologies. Additionally, GraphSPARQL's extension to GraphQL allows using SPARQL filter operations to filter the data in queries. The evaluation showed that GraphSPARQL can compete with existing GraphQL to SPARQL solutions and outperforms them for deeply nested queries.
Towards a component-based acceleration of convolutional neural networks on FPGAs

In recent years, Convolution Neural Networks (CNN) have been extensively adopted in broad Artificial Intelligence (AI) applications and have demonstrated ability and effectiveness in solving learning problems. However, developing high-performance hardware accelerators on Field Programmable Gate Array (FPGA) for CNNs often demands skills in hardware design and verification, accurate distribution localization, and long development cycles. Besides, the depth of CNN architectures increases by reusing and replicating several layers. In this work, we take advantage of the replication of CNN layers to achieve improvement in design performance and productivity. We propose a programming flow for CNNs on FPGA to generate high-performance accelerators by assembling CNN pre-implemented components as a puzzle based on the graph topology. Using pre-implemented components allows us to use minimum of resources, predict the performance, and gain in productivity since there is no need to synthesize any Hardware Description Language (HDL) source code. Furthermore, the pre-implemented components are reused for different range of applications, reducing the engineering time. Through prototyping, we demonstrate the viability and relevance of our approach. Experiments show a productivity improvement of up to 69% compared to a traditional FPGA implementation while achieving over 1.75× higher Fmax with lower resources and higher energy efficiency.
QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms

Service-oriented computing (SOC) activates communication through web services to provide computing as a service for business applications in the service-oriented architecture (SOA). To make SOC successful, finding a needed service to build a system directly depending on the collection of services is a critical confront. In this paper, the authors planned the clustering-based approach called dynamic clustering (DCLUS). The novelty in DCLUS compared to static-based clustering technique is the use of dynamic clustering technique. In existing CLUS, the static various widths clustering method is exploited for the users and services clustering. However, due to the limitations of static clustering, they proposed dynamic clustering to optimize the performance of clustering using data mining to find the associations and patterns, for services, and also the prediction accuracy. The performance of the proposed DCLUS system will be implemented and evaluated facing the existing system in phases of precision, recall, and f-score performance metrics using the research dataset.
Using intra‐task flexibility on an intelligent tutoring system to promote arithmetic problem‐solving proficiency

Abstract Problem solving is often regarded as one of the most essential cognitive functions in our daily lives, and, for that reason, educational theorists have long stressed the need for its development. As cognitive flexibility is a fundamental characteristic necessary throughout the problem‐solving process, the purpose of this study is to analyse students' problem‐solving performance after following intra‐task flexibility‐based training mediated by an intelligent tutoring system. With this aim, 110 fifth and sixth grade students took part in a quasi‐experimental study that included six forty‐five‐minute flexibility‐based training sessions preceded and followed by test sessions in order to evaluate eventual problem‐solving proficiency improvements. The findings show that intra‐task flexibility training enhances students' ability to solve arithmetic word problems, especially when flexibility‐related activities are completed sequentially in the same session and not in different sessions. Furthermore, the proposed instruction is particularly helpful in improving girls' problem‐solving competence, which can minimize eventual gender gaps and strengthen their STEM vocation. Practitioner notes What is already known about this topic Cognitive flexibility is beneficial in finding appropriate problem‐solving strategies. Exposing students to a variety of problem‐solving methods or strategies can improve their cognitive flexibility. Boys are more likely than girls to flexibly apply multiple solution paths while solving problems, which leads to gender differences in mathematics. By contrast, girls are more prone to take advantage of instructional advice when using intelligent tutoring systems (ITS). What this paper adds Prompting students to apply multiple resolution strategies within the same problem (intra‐task flexibility instruction) is comparatively more efficient than standard instruction in promoting problem‐solving performance. It is more effective in terms of problem‐solving proficiency to ask students to find different resolutions for a problem sequentially in the same session than to do so in separate sessions. Effectiveness of ITS‐mediated flexibility‐based training leads to the promotion of problem‐solving proficiency, particularly in the case of female students. Implications for practice and/or policy The use of the ITS HINTS has the potential to promote the use of multiple solutions in problem solving by monitoring the resolution paths of the student, which can be difficult in a traditional classroom setting. ITS‐mediated flexibility‐based training is especially effective in improving girls' problem‐solving competence, which can minimize eventual gender gaps and strengthen their STEM vocation.
Adaptive granulation Renyi rough entropy image thresholding method with nested optimization

The rough entropy based image thresholding algorithm can effectively deal with the uncertainty information in an image. Nevertheless, the existing rough entropy based thresholding methods have two limitations. One is that there is no generalized rough entropy definition. The other is that the granule size cannot be selected automatically in combination with the image information in the granulation process. In order to address these two problems, an adaptive granulation Renyi rough entropy image thresholding method with nested optimization is proposed in this paper. First, a definition of generalized rough entropy with parameter based on Renyi entropy form is proposed to describe the uncertainty information of complex images. Second, in order to select the granule size in combination with the specific image information, a granule size selection method is proposed by maximizing the uniformity of the segmented image regions. Finally, a nested optimization adaptive granulation Renyi rough entropy thresholding segmentation algorithm is proposed. In the experiments, the Renyi rough entropy is compared with the existing four rough entropy in image thresholding. The new parametric rough entropy can obtain better segmentation results than the other existing rough entropy. The novel adaptive granulation Renyi rough entropy thresholding segmentation algorithm is compared with the state-of-the-art image segmentation methods on several different image data sets, which verifies the effectiveness of the novel algorithm. • Define a Renyi rough entropy measure. • Propose the minimized Renyi rough entropy thresholding (RRET) method. • Propose an adaptive granule size selection method. • Propose an adaptive granulation RRET algorithm with nested optimization.
An empirical evaluation of machine learning techniques to classify code comprehension based on EEG data

Code comprehension consists of a cognitive process in which developers invest mental effort to grasp source code snippets. The literature evidence that electroencephalogram (EEG) signals correlate with developers’ code comprehension. An effective machine learning technique that classifies code comprehension based on EEG would be a means for recommendation systems to suggest an appropriate task for the developer. However, there is a lack of empirical studies to investigate the effectiveness of machine learning techniques for classifying developers’ code comprehension based on their EEG data. This study conducts an empirical study to analyze the effectiveness of machine learning techniques to classify developers’ code comprehension trained with EEG Data. A wireless EEG device was used to collect brainwaves from software developers while performing code comprehension tasks. The generated data set was used to train a K-Nearest Neighbor (KNN), Neural Network (NN), a Naïve Bayes (NB), a Random Forest (RF), and a Support Vector Machine (SVM) classifier. We measured the effectiveness of these techniques using precision, recall, and f-measure. Furthermore, we analyzed the mean differences of f-measure between classifiers to check if the classifiers’ effectiveness is significantly higher than the f-measure mean of a random guessing classifier and higher than a high effectiveness threshold of 80%. After we trained the classifiers using 10-fold cross-validation, the K-Nearest Neighbor classifier obtained an f-measure mean of 86% to classify code comprehension. Moreover, the results also show that KNN, NN, and RF techniques were able to classify code comprehension based on developers’ EEG data higher with an f-measure above 80%.
Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing

With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2∼25 times better performance compared with existing algorithms.
GEN: Generative Equivariant Networks for Diverse Image-to-Image Translation

Image-to-image (I2I) translation has become a key asset for generative adversarial networks. Convolutional neural networks (CNNs), despite having a significant performance, are not able to capture the spatial relationships among different parts of an object and, thus, do not qualify as the ideal representative model for image translation tasks. As a remedy to this problem, capsule networks have been proposed to represent patterns for a visual object in such a way that preserves hierarchical spatial relationships. The training of capsules is constrained by learning all pairwise relationships between capsules of consecutive layers. This design would be prohibitively expensive both in time and memory. In this article, we present a new framework for capsule networks to provide a full description of the input components at various levels of semantics, which can successfully be applied to the generator-discriminator architectures without incurring computational overhead compared to the CNNs. To successfully apply the proposed capsules in the generative adversarial network, we put forth a novel Gromov-Wasserstein (GW) distance as a differentiable loss function that compares the dissimilarity between two distributions and then guides the learned distribution toward target properties, using optimal transport (OT) discrepancy. The proposed method-which is called generative equivariant network (GEN)-is an alternative architecture for GANs with equivariance capsule layers. The proposed model is evaluated through a comprehensive set of experiments on I2I translation and image generation tasks and compared with several state-of-the-art models. Results indicate that there is a principled connection between generative and capsule models that allows extracting discriminant and invariant information from image data.