Reviewerly/exHarmony huggingface dataset:
Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

These days, investigations of information are becoming essential for various associations all over the globe.By and large, different associations need to perform information examinations on their joined data sets.Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data.Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals' crude information.We devised two unique protocols for frequent mining itemsets in horizontally partitioned datasets while maintaining privacy.In such a scenario, data possessors outwork mining tasks on their multiparty data by preserving privacy.The proposed framework model encompasses two or more data possessors who encrypt their information and dispense their encrypted data to two or more clouds by a data share allocator algorithm.This methodology protects the data possessor's raw data from other data possessors and the other clouds.To guarantee data privacy, we plan a proficient enhanced homomorphic encryption conspire.Our approach ensures privacy during communication and accumulation of data and guarantees no information or data adversity and no incidental consequences for data utility.Therefore, the advantages of data mining have remained redesigned.To approve the exhibition of our protocols, we implemented the protocols through broad experiments, where the assessment outcome showed that the mined results obtained by our protocols are reliable to those obtained by a traditional sole-machine approach.Meanwhile, the findings of our performance assessment have shown that our methodology is very efficient, with reasonably reduced communication time and computation costs.
RDCTrans U-Net: A Hybrid Variable Architecture for Liver CT Image Segmentation

Segmenting medical images is a necessary prerequisite for disease diagnosis and treatment planning. Among various medical image segmentation tasks, U-Net-based variants have been widely used in liver tumor segmentation tasks. In view of the highly variable shape and size of tumors, in order to improve the accuracy of segmentation, this paper proposes a U-Net-based hybrid variable structure-RDCTrans U-Net for liver tumor segmentation in computed tomography (CT) examinations. We design a backbone network dominated by ResNeXt50 and supplemented by dilated convolution to increase the network depth, expand the perceptual field, and improve the efficiency of feature extraction without increasing the parameters. At the same time, Transformer is introduced in down-sampling to increase the network's overall perception and global understanding of the image and to improve the accuracy of liver tumor segmentation. The method proposed in this paper tests the segmentation performance of liver tumors on the LiTS (Liver Tumor Segmentation) dataset. It obtained 89.22% mIoU and 98.91% Acc, for liver and tumor segmentation. The proposed model also achieved 93.38% Dice and 89.87% Dice, respectively. Compared with the original U-Net and the U-Net model that introduces dense connection, attention mechanism, and Transformer, respectively, the method proposed in this paper achieves SOTA (state of art) results.
The Metaverse—An Alternative Education Space

The Metaverse is a 3D virtual environment already populated by our students. In the form of avatars, their unique personas happily collaborate in spaces such as Roblox and Minecraft. After two years of being fully online, remote learning became associated with fatigue from business-model video conferencing tools such as Zoom, Microsoft Teams, and Google Meet. It is time to consider the adoption of customizable Metaverse platforms where educators and their students can safely teach and learn again in the same space. This paper examines contagion theory to frame the breakdown of our classroom environment during the Covid-19 Pandemic and the transition back to a mosaic of hybrid contexts. This paper delineates the merits of the Metaverse as an alternative education space that fosters Universal Design for learning. Additionally, this paper reviews platforms that support multiple entry points for engagement, representation, action, and expression.
A Selective Ensemble Approach for Accuracy Improvement and Computational Load Reduction in ANN-Based PV Power Forecasting

Day-ahead power forecasting is an effective way to deal with the challenges of increased penetration of photovoltaic power into the electric grid, due to its non-programmable nature.This is significantly beneficial for smart grid and micro-grids application.Machine learning and hybrid approaches are well assessed techniques, able to provide effective forecasting with a data-driven approach based on previous measurements from existing power plants.Ensemble methods can be employed to increase solar power forecasting accuracy, by running several independent forecasting models in parallel.In this paper, a novel selective approach is proposed and assessed, where independently trained neural networks are evaluated in terms of accuracy, in order to properly select a suitable forecasting.Moreover, in order to reduce the associated computational burden, suitably developed new normalization approaches are proposed and evaluated.The considered experimental case study shows that the combination of the proposed procedures is able to increase accuracy and to mitigate the overall computational load, resulting in a simple and lightweight algorithm.Additionally, a comparison with other commonly used techniques has shown that the proposed approach is robust with respect to dataset limited size and discontinuities.
Machine learning denoising of high-resolution X-ray nanotomography data

High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields.
Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases

Abstract Organizations often collect private data and release aggregate statistics for the public’s benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms—and in turn the disclosure risk for people described in the dataset—requires a data curator to choose a value for a privacy budget parameter, ɛ . However, there is little formal guidance for choosing ɛ , a task that requires reasoning about the probabilistic privacy–utility tradeoff. Furthermore, choosing ɛ in the context of statistical inference requires reasoning about accuracy trade-offs in the presence of both measurement error and differential privacy (DP) noise. We present Vi sualizing P rivacy (ViP), an interactive interface that visualizes relationships between ɛ , accuracy, and disclosure risk to support setting and splitting ɛ among queries. As a user adjusts ɛ , ViP dynamically updates visualizations depicting expected accuracy and risk. ViP also has an inference setting, allowing a user to reason about the impact of DP noise on statistical inferences. Finally, we present results of a study where 16 research practitioners with little to no DP background completed a set of tasks related to setting ɛ using both ViP and a control. We find that ViP helps participants more correctly answer questions related to judging the probability of where a DP-noised release is likely to fall and comparing between DP-noised and non-private confidence intervals.
Intelligent Microservice Based on Blockchain for Healthcare Applications

Nowadays, the blockchain, Internet of Things, and artificial intelligence technology revolutionize the traditional way of data mining with the enhanced data preprocessing, and analytics approaches, including improved service platforms.Nevertheless, one of the main challenges is designing a combined approach that provides the analytics functionality for diverse data and sustains IoT applications with robust and modular blockchain-enabled services in a diverse environment.Improved data analytics model not only provides support insights in IoT data but also fosters process productivity.Designing a robust IoT-based secure analytic model is challenging for several purposes, such as data from diverse sources, increasing data size, and monolithic service designing techniques.This article proposed an intelligent blockchain-enabled microservice to support predictive analytics for personalized fitness data in an IoT environment.The designed system support microservice-based analytic functionalities to provide secure and reliable services for IoT.To demonstrate the proposed model effectiveness, we have used the IoT fitness application as a case study.Based on the designed predictive analytic model, a recommendation model is developed to recommend daily and weekly diet and workout plans for improved body fitness.Moreover, the recommendation model objective is to help trainers make future health decisions of trainees in terms of workout and diet plan.Finally, the proposed model is evaluated using Hyperledger Caliper in terms of latency, throughput, and resource utilization with varying peers and orderer nodes.The experimental result shows that the proposed model is applicable for diverse resourceconstrained blockchain-enabled IoT applications and extensible for several IoT scenarios.
On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation

In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks.However, in the area of Automated Essay Scoring (AES), pre-trained models such as BERT have not been properly used to outperform other deep learning models such as LSTM.In this paper, we introduce a novel multi-scale essay representation for BERT that can be jointly learned.We also employ multiple losses and transfer learning from out-of-domain essays to further improve the performance.Experiment results show that our approach derives much benefit from joint learning of multi-scale essay representation and obtains almost the state-of-the-art result among all deep learning models in the ASAP 1 task.Our multi-scale essay representation also generalizes well to CommonLit Readability Prize (CRP 2 ) data set, which suggests that the novel text representation proposed in this paper may be a new and effective choice for long-text tasks.
Comprehensive Overview of Backpropagation Algorithm for Digital Image Denoising

Artificial ANNs (ANNs) are relatively new computational tools used in the development of intelligent systems, some of which are inspired by biological ANNs, and have found widespread application in the solving of a variety of complex real-world problems. It boasts enticing features as well as remarkable data processing capabilities. In this paper, a comprehensive overview of the backpropagation algorithm for digital image denoising was discussed. Then, we presented a probabilistic analysis of how different algorithms address this challenge, arguing that small ANNs can denoise small-scale texture patterns almost as effectively as their larger equivalents. The results also show that self-similarity and ANNs are complementary paradigms for patch denoising, as demonstrated by an algorithm that effectively complements BM3D with small ANNs, surpassing BM3D at a low cost. Here, one of the most significant advantages of this learning technique is that, once taught, digital images may be recovered without prior knowledge of the degradation model (noise/blurring) that caused the digital image to become distorted.
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization

We present FACTPEGASUS, an abstractive summarization model that addresses the problem of factuality during pre-training and finetuning: (1) We augment the sentence selection strategy of PEGASUS's (Zhang et al., 2020) pre-training objective to create pseudosummaries that are both important and factual;(2) We introduce three complementary components for fine-tuning.The corrector removes hallucinations present in the reference summary, the contrastor uses contrastive learning to better differentiate nonfactual summaries from factual ones, and the connector bridges the gap between the pre-training and finetuning for better transfer of knowledge.Experiments on three downstream tasks demonstrate that FACTPEGASUS substantially improves factuality evaluated by multiple automatic metrics and humans.Our thorough analysis suggests that FACTPEGASUS is more factual than using the original pre-training objective in zero-shot and few-shot settings, retains factual behavior more robustly than strong baselines, and does not rely entirely on becoming more extractive to improve factuality.
Quantum-Logic Gate between Two Optical Photons with an Average Efficiency above 40%

Optical qubits uniquely combine information transfer in optical fibers with a good processing capability and are therefore attractive tools for quantum technologies. A large challenge, however, is to overcome the low efficiency of two-qubit logic gates. The experimentally achieved efficiency in an optical controlled not (cnot) gate reached approximately 11% in 2003 and has seen no increase since. Here, we report on a new platform that was designed to surpass this long-standing record. The new scheme avoids inherently probabilistic protocols and, instead, combines aspects of two established quantum nonlinear systems: atom-cavity systems and Rydberg electromagnetically induced transparency. We demonstrate a cnot gate between two optical photons with an average efficiency of 41.7(5)% at a postselected process fidelity of 81(2)%. Moreover, we extend the scheme to a cnot gate with multiple target qubits and produce entangled states of presently up to five photons. All these achievements are promising and have the potential to advance optical quantum information processing in which almost all advanced protocols would profit from high-efficiency logic gates.1 MoreReceived 8 December 2021Accepted 20 April 2022DOI:https://doi.org/10.1103/PhysRevX.12.021035Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasElectromagnetically induced transparencyQuantum gatesQuantum information with atoms & lightPhysical SystemsRydberg atoms & moleculesAtomic, Molecular & OpticalQuantum Information
How should metaverse augment humans with disabilities?

The metaverse is a new paradigm made possible by emerging technologies like virtual reality, augmented reality, and blockchains. Humans with disabilities should not be excluded from the metaverse and should have equitable access to it. However, it is unclear which options humans with physical and cognitive disabilities will have in the current metaverse to use and represent themselves – and feel included. Our research seeks to determine how humans with disabilities should be augmented in the metaverse, as well as whether the current metaverse permits such augmentations. If not, what features should be added to make the metaverse more accessible and inclusive? In this regard, we have begun to investigate one of the most well-known decentralized metaverses, namely Decentraland.
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing approaches typically follow the prototype learning paradigm to perform meta-inference, which fails to fully exploit the underlying information from support image-mask pairs, resulting in various segmentation failures, e.g., incomplete objects, ambiguous boundaries, and distractor activation. To this end, we propose a simple yet versatile framework in the spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is first implemented on the annotated support image, and then the coarse segmentation mask is divided into multiple regions with different properties. Leveraging effective masked average pooling operations, a series of support-induced proxies are thus derived, each playing a specific role in conquering the above challenges. Moreover, we devise a unique parallel decoder structure that integrates proxies with similar attributes to boost the discrimination power. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information as a guide at the “episode” level, not just about the object cues themselves. Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of DCP over conventional prototype-based approaches (up to 5~10% on average), which also establishes a new state-of-the-art. Code is available at github.com/chunbolang/DCP.
Lessons from infant learning for unsupervised machine learning

The desire to reduce the dependence on curated, labeled datasets and to leverage the vast quantities of unlabeled data has triggered renewed interest in unsupervised (or self-supervised) learning algorithms. Despite improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning and clustering optimizations, unsupervised machine learning still falls short of its hypothesized potential as a breakthrough paradigm enabling generally intelligent systems. Inspiration from cognitive (neuro)science has been based mostly on adult learners with access to labels and a vast amount of prior knowledge. To push unsupervised machine learning forward, we argue that developmental science of infant cognition might hold the key to unlocking the next generation of unsupervised learning approaches. We identify three crucial factors enabling infants' quality and speed of learning: (1) babies' information processing is guided and constrained; (2) babies are learning from diverse, multimodal inputs; and (3) babies' input is shaped by development and active learning. We assess the extent to which these insights from infant learning have already been exploited in machine learning, examine how closely these implementations resemble the core insights, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning. Unsupervised machine learning algorithms reduce the dependence on curated, labeled datasets that are characteristic of supervised machine learning. The authors argue that the developmental science of infant cognition could inform the design of unsupervised machine learning approaches.
The Application of Blockchain in Social Media: A Systematic Literature Review

Social media has transformed the mode of communication globally by providing an extensive system for exchanging ideas, initiating business contracts, and proposing new professional ideas. However, there are many limitations to the use of social media, such as misinformation, lack of effective content moderation, digital piracy, data breaches, identity fraud, and fake news. In order to address these limitations, several studies have introduced the application of Blockchain technology in social media. Blockchains can provides transparency, traceability, tamper-proofing, confidentiality, security, information control, and supervision. This paper is a systematic literature review of papers covering the application of Blockchain technology in social media. To the best of our knowledge, this is the first systematic literature review that elucidates the combination of Blockchain and social media. Using several electronic databases, 42 related papers were reviewed. Our findings show that previous studies on the applications of Blockchain in social media are focused mainly on blocking fake news and enhancing data privacy. Research in this domain began in 2017. This review additionally discusses several challenges in applying Blockchain technologies in social media contexts, and proposes alternative ideas for future implementation and research.
Content-based medical image retrieval system for lung diseases using deep CNNs

Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses.