Reviewerly/exHarmony huggingface dataset:
Effectiveness of Augmented Reality in Online Distance Learning at the Time of the COVID-19 Pandemic

The current study seeks to investigate the effectiveness of augmented reality in online distance learning at the time of the COVID-19 pandemic. The study was carried out in an instructional software authoring tools course for in-structional technology department students to develop academic achievement and instructional software design skills. To achieve this, augmented reality was used, which allowed the learning process to be completed via online dis-tance learning. Forty students were selected to participate by purposeful sampling. The results show that augmented reality was more effective in supporting academic achievement as well as skills acquisition in the virtual classrooms compared to the virtual classrooms that did not use augmented reality in online distance learning. In general, augmented reality or one of its (fixed, animated) patterns can be used to effectively learn in online distance learning during pandemic periods. As well as augmented reality that can use location-aware or vision-based maybe have a good potential in online dis-tance learning within the pandemic. As such the teachers must be prepared to employ all patterns of augmented reality in traditional online distance learn-ing or during the pandemics to achieve the need for 21st-century skills. Con-ducting more research is required in online distance learning by using aug-mented reality with another sample and different content general, augmented reality or one of its (fixed, animated) patterns can be used to effectively learn in online distance learning during pandemic periods.
Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible.&#x0D; Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.
<i>Did Aristotle Use a Laptop?</i>A Question Answering Benchmark with Implicit Reasoning Strategies

Abstract A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of ∼ 66%.
Mining Sequential Patterns in Uncertain Databases Using Hierarchical Index Structure

In this uncertain world, data uncertainty is inherent in many applications and its importance is growing drastically due to the rapid development of modern technologies. Nowadays, researchers have paid more attention to mine patterns in uncertain databases. A few recent works attempt to mine frequent uncertain sequential patterns. Despite their success, they are incompetent to reduce the number of false-positive pattern generation in their mining process and maintain the patterns efficiently. In this paper, we propose multiple theoretically tightened pruning upper bounds that remarkably reduce the mining space. A novel hierarchical structure is introduced to maintain the patterns in a space-efficient way. Afterward, we develop a versatile framework for mining uncertain sequential patterns that can effectively handle weight constraints as well. Besides, with the advent of incremental uncertain databases, existing works are not scalable. There exist several incremental sequential pattern mining algorithms, but they are limited to mine in precise databases. Therefore, we propose a new technique to adapt our framework to mine patterns when the database is incremental. Finally, we conduct extensive experiments on several real-life datasets and show the efficacy of our framework in different applications.
Agricultural data sharing and sustainable development of ecosystem based on block chain

The development of modern science and information technology has promoted social progress and created better conditions for the realization of smart agriculture. The core of smart agriculture is big data, but the current big data management model still has certain hidden problems. The application of block chain technology can better realize the sharing of agricultural data. This article is based on block chain technology to study agricultural data sharing and sustainable development of ecological environment. This article conducts an in-depth analysis of the advantages of block chain-based big data sharing, and establishes a block chain-based big data sharing model. In order to improve the problems of significant regional and independent information of major agricultural information websites, this article also analyzes the agricultural data sharing technology, develops an agricultural information sharing system combined with the cloud computing platform, and links the characteristics of agricultural data to common data integration and the sharing method has been improved. According to the experimental data in the agricultural information exchange platform, users' attention to agricultural product production data is as high as 26.2%, and their attention to agricultural consumption accounts for 25.6%. It can be seen that many users are willing to complete the exchange of agricultural data on the platform. A cross-regional agricultural information exchange platform can improve the scattered distribution of agricultural data in the past, and provide agricultural production decisions for users in need through data analysis, and promote the sustainable development of agricultural economy. The innovation of this article is to make full use of blockchain technology to realize agricultural data sharing and sustainable development of the ecosystem. Through the combination of quantitative analysis and qualitative analysis, theoretical research and empirical research, it highlights decentralization from multiple angles. The mechanism can actually solve agricultural problems and achieve ecological sustainable development.
High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine Function

Approximate homomorphic encryption with the residue number system (RNS), called RNS-variant Cheon-Kim-Kim-Song (RNS-CKKS) scheme [12, 13], is a fully homomorphic encryption scheme that supports arithmetic operations for real or complex number data encrypted. Although the RNS-CKKS scheme is a fully homomorphic encryption scheme, most of the applications with the RNS-CKKS scheme use it as the only leveled homomorphic encryption scheme because of the lack of the practicality of the bootstrapping operation of the RNS-CKKS scheme. One of the crucial problems of the bootstrapping operation is its poor precision. While other basic homomorphic operations ensure sufficiently high precision for practical use, the bootstrapping operation only supports about 20-bit fixed-point precision at best, which is not high precision enough to be used for the reliable large-depth homomorphic computations until now. In this paper, we improve the message precision in the bootstrapping operation of the RNS-CKKS scheme. Since the homomorphic modular reduction process is one of the most important steps in determining the precision of the bootstrapping, we focus on the homomorphic modular reduction process. Firstly, we propose a fast algorithm of obtaining the optimal minimax approximate polynomial of modular reduction function and the scaled sine/cosine function over the union of the approximation regions, called an improved multi-interval Remez algorithm. In fact, this algorithm derives the optimal minimax approximate polynomial of any continuous functions over any union of the finite number of intervals. Next, we propose the composite function method using the inverse sine function to reduce the difference between the scaling factor used in the bootstrapping and the default scaling factor. With these methods, we reduce the approximation error in the bootstrapping of the RNS-CKKS scheme by 1/1176–1/42 (5.4–10.2-bit precision improvement) for each parameter setting. While the bootstrapping without the composite function method has 27.2–30.3-bit precision at maximum, the bootstrapping with the composite function method has 32.6–40.5-bit precision.
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

This paper presents the first large-scale metaevaluation of machine translation (MT).We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020.Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends.An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed.MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable.Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community.After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.
Contrastive Attention for Automatic Chest X-ray Report Generation

Recently, chest X-ray report generation, which aims to automatically generate descriptions of given chest X-ray images, has received growing research interests.The key challenge of chest X-ray report generation is to accurately capture and describe the abnormal regions.In most cases, the normal regions dominate the entire chest X-ray image, and the corresponding descriptions of these normal regions dominate the final report.Due to such data bias, learning-based models may fail to attend to abnormal regions.In this work, to effectively capture and describe abnormal regions, we propose the Contrastive Attention (CA) model.Instead of solely focusing on the current input image, the CA model compares the current input image with normal images to distill the contrastive information.The acquired contrastive information can better represent the visual features of abnormal regions.According to the experiments on the public IU-X-ray and MIMIC-CXR datasets, incorporating our CA into several existing models can boost their performance across most metrics.In addition, according to the analysis, the CA model can help existing models better attend to the abnormal regions and provide more accurate descriptions which are crucial for an interpretable diagnosis.Specifically, we achieve the state-ofthe-art results on the two public datasets.
An Approach to Integrating Sentiment Analysis into Recommender Systems

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.
Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology.During this time, new diagnostic techniques were developed.Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals.The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer.Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival.A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost.In this research, CNN models are employed to analyse imaging data of colon cells.For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized.To determine the learning rate, the models are trained and evaluated at various Epochs.It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively.And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.
FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods

In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.
Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
Privacy-preserving aware data aggregation for IoT-based healthcare with green computing technologies

Despite the rapid development of the Internet of Things technologies where more and more medical sensors and gadgets are connected to the Internet, limited energy resources due to transmission, and security are still main challenges. Most cases, patients wear multiple medical devices that transmit sensed medical data wirelessly to servers, which might cause big traffic on the communication networks, which in turn cause high energy consumption. Therefore, using the data aggregation, we can considerably reduce the energy consumption by eliminating redundant data; yet collected data must be fully protected. Secure data collection and transfer to centralized servers in healthcare applications employing IoT is quite challenging to protect against several attacks for illegal data access. For this reason, massive security measures should be taken to ensure that patients’ data can only be accessed by legitimate users. This paper proposes EPPADA: Efficient Privacy‑Preserving Authentication and Data Aggregation scheme in conjunction with Homomorphic Encryption concepts to meet requirements of healthcare using IoT with green computing technologies. The main objective of this proposed scheme is to decrease the communication overhead and energy consumption while maintaining safe and secure aggregation of the healthcare data between medical sensors and cloud servers. The proposed system is experimentally developed using E-health sensor shield V2.0 platform. Based on security analysis that has the most extensive set of security features, our multi-objective approach enhances the End-to-End Delay, Computational Cost, Communication Overhead, besides maintaining security features.
Balanced Multimodal Learning via On-the-fly Gradient Modulation

Multimodal learning helps to comprehensively understand the world, by integrating different senses. Accordingly, multiple input modalities are expected to boost model performance, but we actually find that they are not fully exploited even when the multimodal model outperforms its uni-modal counterpart. Specifically, in this paper we point out that existing multimodal discriminative models, in which uniform objective is designed for all modalities, could remain under-optimized uni-modal representations, caused by another dominated modality in some scenarios, e.g., sound in blowing wind event, vision in drawing picture event, etc. To alleviate this optimization imbalance, we propose on-the-fly gradient modulation to adaptively control the optimization of each modality, via monitoring the discrepancy of their contribution towards the learning objective. Further, an extra Gaussian noise that changes dynamically is introduced to avoid possible generalization drop caused by gradient modulation. As a result, we achieve considerable improvement over common fusion methods on different multimodal tasks, and this simple strategy can also boost existing multimodal methods, which illustrates its efficacy and versatility. The source code is available at https://github.com/GeWu-Lab/OGM-GE_CVPR2022.
The promise of large language models in health care

The past few months have seen a rapid acceleration of innovation in the field of generative artificial intelligence with large language models (LLMs), capturing academic, media, and public attention. GPT-3, BERT, and ChatGPT are examples of LLMs that have been trained on swathes of the internet to produce impressive responses to human queries. The ability of these generalised LLMs to write fiction, develop computer code, and speculate on the future has prompted recognition that we are approaching artificial general intelligence for the first time.
Discharge Education for Mild Traumatic Brain Injury Patients at Emergency Department

This study aims to review the literature on educational interventions for the discharge of mild traumatic brain injury (mTBI) patients in the Emergency Room (IGD). Database searches were performed based on PICO queries and keywords. using ProQuest, ScienceDirect, Scopus, and PubMed for the period 2012-2019. Ten studies were reviewed. The findings revealed the contents of discharge information for mTBI patients including diagnosis, signs and symptoms, treatment and medication, pain management, home care, and ED return instructions. Four methods of discharge intervention are commonly used in the ED; written, spoken, animated and moving videos, and printed instructions measured over two time periods, either pre-discharge in the ED or follow-up 1-2 weeks post-discharge at home. In conclusion, combined teaching was more effective in terms of level of knowledge, understanding, and ED repeat visits.&#x0D; &#x0D; Keywords: Discharge education, Emergency, Minor Traumatic Brain Injury