Reviewerly/exHarmony huggingface dataset:
Double Attention for Multi-Label Image Classification

Multi-label image classification is an essential task in image processing. How to improve the correlation between labels by learning multi-scale features from images is a very challenging problem. We propose a Double Attention Network (DAN) to improve the correlation between image feature regions and labels, as well as between labels and labels. Firstly, the dynamic learning strategy is used to extract the multi-scale features of the image to solve the problem of inconsistent scale of objects in the image. Secondly, in order to improve the correlation between the image feature regions and the labels, we use the spatial attention module to focus on the important regions of the image to learn their salient features, while we use the channel attention module to model the correlation between the channels to improve the correlation between the labels. Finally, the output features of two attention modules are fused as one multi-label image classification model. Experiments on MS-COCO 2014 dataset, Pascal VOC 2007 dataset and NUS-WIDE dataset demonstrate that our model is significantly better than the state-of-the-art models. Besides, visualization analyses show that our model has a strong ability for image salient feature learning and label correlation capturing.
DGC: Dynamic group behavior modeling that utilizes context information for group recommendation

In the real world, group recommendation that recommends items to a set of users (i.e., a group) is a challenging problem since it is very difficult to ensure the satisfaction of all group members with different preferences. Many existing group recommendation systems commonly use aggregation methods, which are insufficient to model group behavior where preference of a user as an individual is often changed when he/she is a member of a group. Some recent methods attempt to reflect this dynamic group behavior. However, they still have limitations in capturing complex relationships between items and group members. Moreover, previous approaches do not fully utilize useful context information available, and only use rating data. Reflecting context information together with ratings helps resolve a well-known data sparsity problem in group recommendation. In this paper, we propose a novel group recommendation framework called Dynamic Group behavior modeling that utilizes Context information for group recommendation(DGC). In DGC, we newly develop dynamic group behavior modeling that enables summarization of complex patterns in group decision making processes. To apply context information, firstly, we extract relevant context information from a heterogeneous information network (HIN) that contains rich information between various entities. Then, context information is properly applied to a group recommendation model by using semi-supervised learning that is composed of a supervised loss for label prediction and an unsupervised loss for context prediction. Experimental results show that our method provides significant performance improvement over other existing methods.
Image compression using adaptive multiresolution image decomposition algorithm

With the growth of modern digital technologies, demand for transmission multimedia and digital images, which require more storage space and transmission bandwidth, has been increased rapidly. Hence, developing new image compression techniques for reducing data size without degrading the quality of the image, has gained a lot of interest recently. In this study, an adaptive multiresolution image decomposition (AMID) algorithm is proposed and its application for image compression is explored. The developed algorithm is capable of decomposing an image along the vertical, horizontal, and diagonal directions using the pyramidal multiresolution scheme. Compared to the wavelet transform, the AMID can be used for decimation with the guarantees of perfect signal reconstruction. Furthermore, the application of the AMID for image compression is explored and its performance is compared with the state-of-the-art image compression techniques. The performance of compression method is evaluated using peak signal-to-noise ratio and compression ratio. Experimental results have shown promising performance compared with the results of using other image compression approaches.
Determining the Volcanic Eruption Style of Tephra Deposits From Infrared Spectroscopy

Abstract Orbital imagery and spectroscopy at Mars have identified a variety of deposits potentially consistent with volcanic tephra formed during explosive volcanic eruptions, and some of these deposits may have formed due to water‐ or ice‐magma interactions during phreatomagmatic eruptions. If this is the case, these deposits could serve as an additional record of past water on Mars. Previous work has demonstrated that phreatomagmatic tephra is characterized by much lower crystallinities than tephras from other types of eruptions. We hypothesize that crystallinity could be inferred remotely using spectroscopy; however, tephra spectral properties have not been directly linked to their mineralogy. Here, we use Mars analog tephra samples to investigate if eruption styles and the past presence of water during the eruption of possible volcanic deposits on Mars can be determined using orbital spectroscopy. Visible/near‐infrared (VNIR) reflectance and thermal infrared (TIR) emission spectra were collected of basaltic volcanic tephras sourced from a range of eruption styles and deposit types on Earth. Our research demonstrates that, TIR and VNIR data are both sufficient to detect increased glass abundances in volcanic deposits, potentially indicating volatile interactions during an eruption, and that glass‐poor tephras have distinct TIR properties that can be used to infer tephra type (e.g., ignimbrite vs. scoria). Combining VNIR and TIR orbital data for analysis based on our new laboratory spectral endmember library may allow a reevaluation of Martian volcanic and volatile histories using current and future planetary orbital and in situ spectral datasets.
Flexible and anonymous network slicing selection for C-RAN enabled 5G service authentication

The fifth generation (5G) mobile communication technologies significantly promote the development of attractive applications such as automatic driving and telemedicine due to its lower latency, higher data rate and massive connectivities. In addition, 5G network slices can be used to realize tailored transmission network according to users’ different requirements. Nevertheless, 5G still suffers from security, privacy and performance issues exemplified by authenticated key agreement, identity and network slice privacy, and the flexibility and efficiency of network slice selection. In this paper, we tackle these challenges by proposing FANS, a Flexible and Anonymous Network Slicing method for cloud radio access network enabled authentication of emerging 5G service. FANS achieves users’ identity privacy protection by hiding the public key associated with the actual identity in transmitted messages. The privacy, flexibility and efficiency of network slice selection are realized by adopting the idea of privacy-aware one-to-many matching which has been used in anonymous attribute-based encryption. Based on comprehensive security and performance analysis, FANS is shown to be secure and efficient.
RETRACTED: 3D Garden landscape planning visualization system based on FPGA processor and virtual reality

Landscape planning is based on science and the arts. Landscape planning and design appropriate coordination to do the relationship between spatial planning and the earth, and between man and nature. Landscape planning has a fundamental goal of sustainable development, in landscape planning, more technologically advanced planning and design theory to solve the modern field's practical problems. It provides a powerful new way to functional space planning and landscape analysis. Geographic Information Systems (GIS), the importance of planning can improve outcomes. Enable planners to understand the natural landscape and accurately understand and landscape planning and landscape design some new ideas. This is the best practical implications of the simulation and verifies the effectiveness of landscape design and planning. The problem for traditional distribution landscape simulation technology, designed a new distribution pattern of rationality simulation technology based on 3D images and virtual reality technology. This analysis method is based on the first time the three-dimensional image of the garden's rational distribution. 3D image analysis of the distribution landscape's effectiveness results from a three-dimensional image reconstruction completed and based on virtual reality technology. Finally, it will discuss the garden, rational drug delivery technologies, case studies, the advantages, and performance test results.
HyperTune

Distributed training is a novel approach to accelerating training of Deep Neural Networks (DNN), but common training libraries fall short of addressing the distributed nature of heterogeneous processors or interruption by other workloads on the shared processing nodes. This paper describes distributed training of DNN on computational storage devices (CSD), which are NAND flash-based, high-capacity data storage with internal processing engines. A CSD-based distributed architecture incorporates the advantages of federated learning in terms of performance scalability, resiliency, and data privacy by eliminating the unnecessary data movement between the storage device and the host processor. The paper also describes Stannis, a DNN training framework that improves on the shortcomings of existing distributed training frameworks by dynamically tuning the training hyperparameters in heterogeneous systems to maintain the maximum overall processing speed in term of processed images per second and energy efficiency. Experimental results on image classification training benchmarks show up to 3.1x improvement in performance and 2.45x reduction in energy consumption when using Stannis plus CSD compare to the generic systems.
WITHDRAWN: A study on policies and implementation of information and communication technology (ICT) in educational systems

As of late, a few investigations and reviews have features the probabilities and the potential advantages of statistics and correspondence advances with regard to ICT for improving the quality of education. ICT is visible as an element on the school training level that would deliver a method to re-examine and modernize the instructive frameworks, instructional designs and procedures. A significant amount of fund is allocated for usage of technology in education under key Government schemes. Schemes such as ICT at Schools have potential for fostering academic growth and upgrading skills of students, which help immensely in their future employability. Several such schemes exist which pertain to technology in education and executed either directly by the state or through private entities. A policy for implementation of ICT was thought out and designed at national level. The paper evaluates the ICT policy and assesses its implementation at School level in terms of effectiveness towards mass education. It recommends suitable measures for improvement in monitoring, implementation based on outcomes of the survey. Outcome of the research study will help the policymakers to understand the intervention of implementation of ICT Scheme in educational systems and will assist the Government to re-strategize the implementation plan in future as per requirement.
A Fast path re-planning method for UAV based on improved A* algorithm

In order to improve the efficiency of path planning for Unmanned Aerial Vehicles (UAV), this paper proposed a method combined of GA and A* algorithm for three-dimensional path planning, which is divided into global path planning and real-time path re-planning. Global path planning focus on safety and optimality, and provides an optimal path for the UAV that meets the constraints by using the real number gene coding method. Real-time path re-planning focuses on emergency threats and needs to ensure real-time performance. It is implemented by combining the two-dimensional A* algorithm and height planning that improves the searching efficiency compared to traditional three-dimensional A* methods. Finally, simulation results show that the proposed method in this paper can effectively reduce the search space and improve the search efficiency.
A Rasch Model and Rating System for Continuous Responses Collected in Large-Scale Learning Systems

An extension to a rating system for tracking the evolution of parameters over time using continuous variables is introduced. The proposed rating system assumes a distribution for the continuous responses, which is agnostic to the origin of the continuous scores and thus can be used for applications as varied as continuous scores obtained from language testing to scores derived from accuracy and response time from elementary arithmetic learning systems. Large-scale, high-stakes, online, anywhere anytime learning and testing inherently comes with a number of unique problems that require new psychometric solutions. These include (1) the cold start problem, (2) problem of change, and (3) the problem of personalization and adaptation. We outline how our proposed method addresses each of these problems. Three simulations are carried out to demonstrate the utility of the proposed rating system.
Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report

Computing Curriculum models have been historically framed from a cognitive perspective. Typically, the focus has been on bodies of knowledge and increasingly the notion of supporting skills and abilities. More recent curriculum documents such as the IT2017 curriculum have moved towards a professional competency approach and have included the further important notion of dispositions.While these dispositions are seen as complementing and supporting the traditional elements of knowledge and skills, exactly how they are conceptualised and operationalised is still being developed. The ACM/IEEE-CS Computing Curriculum 2020 Overview Project (CC2020) has been working towards this goal, in part driven by the very practical purpose of enabling visualisation of the multiple components of the existing and differing curriculum reports, incorporating the notion of dispositions and enabling them to be mapped to clusters of knowledge and skills embodied in selected curriculum statements.This paper reports on progress to date towards conceptualising and operationalising the notion of dispositions within the CC2020 project, reviews the research challenges faced, the models adopted and the findings to date.
Quantitatively Analyzing Relay Networks in Bitcoin

Bitcoin's throughput is far lower than those of centralized systems such as Visa and PayPal. To handle a comparable number of transactions, Bitcoin's throughput must be improved, which will require a reduction in the block propagation time. Relay networks have attracted attention as a method to improve throughput. However, the nature and strength of the effects of relay networks are not yet clear. In particular, the effects on individual nodes have received little attention. Thus, this study aims to investigate the effects of relay networks on the Bitcoin network and its nodes under the current network state in approximately 2019. We simulate a relay network under the current network state because simulation is virtually the only way to obtain data for all nodes. A major contribution of this study is that it is the first to investigate the relay network effect on the 90th percentile of block propagation time. Moreover, this study shows that relay networks benefit not only the system but also the nodes utilizing them. The utilizing nodes' blocks were more than six times more likely to be approved as valid blocks when nonutilizing and utilizing nodes generated blocks simultaneously. This finding can motivate the use of nodes in relay networks.
The Expressive Power of Graph Neural Networks as a Query Language

In this paper we survey our recent results characterizing various graph neural network (GNN) architectures in terms of their ability to classify nodes over graphs, for classifiers based on unary logical formulas- or queries. We focus on the language FOC2, a well-studied fragment of FO. This choice is motivated by the fact that FOC2 is related to theWeisfeiler-Lehman (WL) test for checking graph isomorphism, which has the same ability as GNNs for distinguishing nodes on graphs. We unveil the exact relationship between FOC2 and GNNs in terms of node classification. To tackle this problem, we start by studying a popular basic class of GNNs, which we call AC-GNNs, in which the features of each node in a graph are updated, in successive layers, according only to the features of its neighbors. We prove that the unary FOC2 formulas that can be captured by an AC-GNN are exactly those that can be expressed in its guarded fragment, which in turn corresponds to graded modal logic. This result implies in particular that ACGNNs are too weak to capture all FOC2 formulas. We then seek for what needs to be added to AC-GNNs for capturing all FOC2. We show that it suffices to add readouts layers, which allow updating the node features not only in terms of its neighbors, but also in terms of a global attribute vector. We call GNNs with readouts ACR-GNNs. We also describe experiments that validate our findings by showing that, on synthetic data conforming to FOC2 but not to graded modal logic, AC-GNNs struggle to fit in while ACR-GNNs can generalise even to graphs of sizes not seen during training.
CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text

Abstract Background Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p &lt; 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions We have developed a web-based summarization and visualization tool, CERC ( https://newton.isye.gatech.edu/CERC1/ ), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.
Using Argumentation to Obtain and Explain Results in a Decision Support System

We introduce an application for a general-purpose decision support system in which the user can express conditional preference rules involving several comparison criteria among alternatives. The system uses rules and available evidence to build arguments, performs an argumentation-based analysis to entail the best alternatives, and explains how it arrives at its conclusions.
English outside the academic sphere: A mobile‐based context‐aware comparison study on collaborative and individual learning

Abstract Context‐aware ubiquitous learning (CAUL) technology provides language learners with interactive learning environments and has been found to increase learning effectiveness and self‐efficacy due to student interaction, discussion and evaluation of the entire learning process. This study used a mobile‐based ubiquitous learning system combined with a collaborative learning approach to develop Fitness‐specific English listening and reading, and fitness knowledge. The researchers recruited two groups of participants, an individual learning group ( N = 31) and a collaborative learning group ( N = 30), and evaluated their learning performance via pre‐and post‐tests. In addition, a questionnaire explored the perceived usefulness, usability, follow‐up intention of using the system and self‐efficacy regarding fitness‐specific English. The results show that both groups improved fitness‐specific English in terms of listening and reading comprehension. Moreover, collaborative learning was found to facilitate fitness‐specific knowledge, and those in the collaborative learning group improved their self‐efficacy more than those in the individual learning group.
IoT-Based Healthcare Monitoring Using Blockchain

The Internet of Things (IoT) is used to improve traditional healthcare systems in different aspects, including monitoring patients' behaviors. Information gathered by sensors in the IoT plays an essential role in healthcare systems. Because of privacy and security issues, the data must be protected against unauthorized changes. On the other hand, Blockchain technology provides a wide range of mechanisms to protect data against changes. Therefore, IoT-based healthcare monitoring using Blockchain constitutes an exciting technological innovation, which may help mitigate security and privacy concerns related to the gathering of information during patient monitoring. In this chapter, the potential applications of IoT–Blockchain systems are studied, and then monitoring mechanisms in healthcare systems are analyzed. To this end, a novel architecture based on recently reported solutions is proposed. The proposed architecture, with the aid of computational power obtained from the IoT, Blockchain and artificial intelligence, can be used in a wide range of solutions aimed at managing the coronavirus disease 2019 (COVID-19). In order to show the potential of the proposed architecture, three case studies are presented. At the end of this chapter, other applications of the proposed architecture are summarized, which can be used in pandemic situations.