Reviewerly/exHarmony huggingface dataset:
A discovery system for narrative query graphs: entity-interaction-aware document retrieval

Abstract Finding relevant publications in the scientific domain can be quite tedious: Accessing large-scale document collections often means to formulate an initial keyword-based query followed by many refinements to retrieve a sufficiently complete, yet manageable set of documents to satisfy one’s information need. Since keyword-based search limits researchers to formulating their information needs as a set of unconnected keywords, retrieval systems try to guess each user’s intent. In contrast, distilling short narratives of the searchers’ information needs into simple, yet precise entity-interaction graph patterns provides all information needed for a precise search. As an additional benefit, such graph patterns may also feature variable nodes to flexibly allow for different substitutions of entities taking a specified role. An evaluation over the PubMed document collection quantifies the gains in precision for our novel entity-interaction-aware search. Moreover, we perform expert interviews and a questionnaire to verify the usefulness of our system in practice. This paper extends our previous work by giving a comprehensive overview about the discovery system to realize narrative query graph retrieval.
Research on Signal Detection of OFDM Systems Based on the LSTM Network Optimized by the Improved Chameleon Swarm Algorithm

In order to improve the signal detection capability of orthogonal frequency-division multiplexing systems, a signal detection method based on an improved LSTM network for OFDM systems is proposed. The LSTM network is optimized by the Chameleon Swarm Algorithm (CLCSA) with the coupling variance and lens-imaging learning. The signal detection method based on the traditional LSTM network has the problem of a complex manual tuning process and insufficient stability. To solve the above problem, the improved Chameleon Swarm Algorithm is used to optimize the initial hyperparameters of the LSTM network and obtain the optimal hyperparameters. The optimal hyperparameters initialize the CLCSA-LSTM network model and the CLCSA-LSTM network model is trained. Finally, the trained CLCSA-LSTM network model is used for signal detection in the OFDM system. The simulation results show that the signal detection performance of the OFDM receiver has been significantly improved, and the dependence on CP and pilot overhead can be reduced. Under the same channel environment, the proposed method in this paper has better performance than other signal detection methods, and is close to the performance of the MMSE method, but it does not need prior statistical characteristics of the channel, so it is easy to implement.
Progressively Hybrid Transformer for Multi-Modal Vehicle Re-Identification

Multi-modal (i.e., visible, near-infrared, and thermal-infrared) vehicle re-identification has good potential to search vehicles of interest in low illumination. However, due to the fact that different modalities have varying imaging characteristics, a proper multi-modal complementary information fusion is crucial to multi-modal vehicle re-identification. For that, this paper proposes a progressively hybrid transformer (PHT). The PHT method consists of two aspects: random hybrid augmentation (RHA) and a feature hybrid mechanism (FHM). Regarding RHA, an image random cropper and a local region hybrider are designed. The image random cropper simultaneously crops multi-modal images of random positions, random numbers, random sizes, and random aspect ratios to generate local regions. The local region hybrider fuses the cropped regions to let regions of each modal bring local structural characteristics of all modalities, mitigating modal differences at the beginning of feature learning. Regarding the FHM, a modal-specific controller and a modal information embedding are designed to effectively fuse multi-modal information at the feature level. Experimental results show the proposed method wins the state-of-the-art method by a larger 2.7% mAP on RGBNT100 and a larger 6.6% mAP on RGBN300, demonstrating that the proposed method can learn multi-modal complementary information effectively.
ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation

In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.
Empirical Studies on the Metaverse-Based Education: A Systematic Review

Metaverse is a digital space achieved through a virtual environment that allows people to enjoy immersive experiences and interactions. Metaverse is changing the world in different fields, from education to health. However, using the Metaverse for educational purposes has rarely been discussed. While there are only a few reviews summarizing the findings of studies on the Metaverse, no study has systematically analyzed empirical studies of the Metaverse in education. This study presents a systematic review of empirical studies on Metaverse-based education to fill this gap. The results revealed that Empirical studies were conducted with university students at most, and Additionally, no study focused on middle and primary school students. The results showed that virtual reality is the most common metaverse type used in empirical studies. However, there is a need for empirical work using Mixed reality technology in the educational Metaverse. As a result of examining the student engagement findings in the studies included in the review, it was seen that few experimental studies focused on students' behavioral engagement. It is expected that the findings of this study will guide empirical research on the Metaverse in education.
MLO Mammogram Pectoral Masking with Ensemble of MSER and Slope Edge Detection and Extensive Pre-Processing

Breast Cancer is a fatal disease. Several people are losing their lives as a result of Breast Cancer. Mammography is the most often used Breast screening modality where we can see both mass and microcalcifications and both are the two major indicators of Breast Cancer. We can see Pectoral muscle also on MLO Mammograms. Digital Image Processing based computer aided diagnosis systems are being used widely to help the radiologist in detecting mass and microcalcifications in MLO Mammograms. However, because the intensity levels of the Pectoral muscle are similar to masses, in computer aided diagnosis system, Pectoral presence in the Mammogram has a detrimental effect on identifying mass. Therefore, in computer aided diagnosis system, Pectoral muscle masking substantially enhances lesion detection. This study suggests a novel ensemble computer aided diagnosis system strategy that combines the MSER based and SlopeEdgeDetection methods with extensive pre-processing to identify and cover Pectoral muscle from MLO Mammograms. The results demonstrate that the new procedure is straightforward and improves the precision of Pectoral region covering. Compared to the average accuracy of the state-of-the-art solutions which is 94%, the suggested technique achieves an accuracy of 99%. Performance analysis makes use of the Mini-MIAS database.
A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices

In the last six years, there has been a rise in research interest with regard to the applications of blockchain technology in supply chains and how these applications bring benefits to supply chain management. In a broader sense, an essential research focus that has been discussed in the literature is the way in which this emerging blockchain technology in supply chains brings sustainable benefits to a community. The rationale for incorporating cloud technology into a blockchain and integrating the blockchain with machine learning for supply chain applications is to maintain technical sustainability. While previous studies suggested and reported sustainable practices of applying blockchain technology in supply chains, the means with which these practices are brought about by the cloud-based blockchain integrated with machine learning (CBML) have not been thoroughly explored in the literature. The case study presented in this paper aims to fill this gap by exploring technically, environmentally, economically, and socially sustainable practices through the use cases of CBML for supply chain management by the international leading container shipping company Maersk. The use cases by Maersk presented in published documents were collected from the Internet and then analyzed. This document analysis was performed in two ways. The first way was a technical review of the blockchain technology used by Maersk with a consideration of technical sustainability to ensure scalability and big data analytics. The other way was to analyze the applications of the CBML by Maersk to indicate how environmental sustainability, economic sustainability, and social sustainability can be achieved. On the other hand, this paper also highlights the negative technical, environmental, economic, and social sustainability impacts caused by Maersk and discusses implications for future research directions.
The Impact of the Broad Money Supply (M2) on Economic Growth per Capita in Palestine

This study examines the effect of the broad money supply (M2) on the per capita economic growth in Palestine using time series data from 2000 to 2020. The study used autoregressive distributed lag model (ARDL), the cointegration approach and the error correction model to investigate the effect of money supply on gross domestic product (GDP) per capita. The model is determined by four macroeconomic variables, namely, gross domestic product (GDP) per capita, broad money supply (M2), gross fixed capital formation (GFCF), and inflation rate (INF). The results show that the money supply, the total capital formation, and the inflation rate have a positive impact on the economic growth in the short run. However, none of these variables affect the economic growth in the long term.
Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.
IPFS: An Off-Chain Storage Solution for Blockchain

Due to intrinsic properties such as transparency, immutability, decentralization, and cryptographic security, blockchain has emerged as a revolutionary technology. Despite its widespread application, blockchain is not used to store huge data due to the restricted storage capacity offered by each block. Furthermore, because the data contained in a block must be duplicated on many other nodes on the network, storage space is wasted. Off-chain storage is therefore required to store significant volumes of data while preserving the efficiency and performance of the network. Additionally, off-chain solutions such as Inter-Planetary File System (IPFS) would reduce the cost requirements of blockchain nodes as data stored and processed by these nodes would be reduced. Hence, strong decentralized storage is provided by IPFS that is a P2P-based content-addressed file sharing system that uses cryptographic hashes to store data. As a result, by combining blockchain with IPFS, an efficient file sharing system would be created. This paper presents a comprehensive IPFS architecture, a simplified version of this integrated approach, and potential applications that might assist to maximize the worth of both systems.
A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions &amp; Recommendations

In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help understand the attributes that play crucial roles in insider threat detection. In addition, they can be beneficial for testing the newly designed security solutions in this domain. We also present recommendations for establishing a baseline standard for analyzing insider-threat data sets. This baseline standard could be used in the future to design resilient architectures and provide a road map for organizations to enhance their defense capabilities against insider threats.
Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform

We propose a lightweight end-to-end text-to-speech model using multi-band generation and inverse short-time Fourier transform. Our model is based on VITS, a high-quality end-to-end text-to-speech model, but adopts two changes for more efficient inference: 1) the most computationally expensive component is partially replaced with a simple inverse short-time Fourier transform, and 2) multi-band generation, with fixed or trainable synthesis filters, is used to generate waveforms. Unlike conventional lightweight models, which employ optimization or knowledge distillation separately to train two cascaded components, our method enjoys the full benefits of end-to-end optimization. Experimental results show that our model synthesized speech as natural as that synthesized by VITS, while achieving a real-time factor of 0.066 on an Intel Core i7 CPU, 4.1 times faster than VITS. Moreover, a smaller version of the model significantly outperformed a lightweight baseline model with respect to both naturalness and inference speed. Code and audio samples are available from https://github.com/MasayaKawamura/MB-iSTFT-VITS.
Text Image Super-Resolution Guided by Text Structure and Embedding Priors

We aim to super-resolve text images from unrecognizable low-resolution inputs. Existing super-resolution methods mainly learn a direct mapping from low-resolution to high-resolution images by exploring low-level features, which usually generate blurry outputs and suffer from severe structure distortion for text parts, especially when the resolution is quite low. Both the visual quality and the readability will suffer. To tackle these issues, we propose a new text super-resolution paradigm by recovering with understanding. Specifically, we extract a text-embedding prior and a text-structure prior from the upsampled image by learning to understand the text. The two priors with rich structure information and text-embedding information are then used as auxiliary information to recover the clear text structure. In addition, we introduce a text-feature loss to guide the training for better text recognizability. Extensive evaluations on both screen and scene text image datasets show that our method largely outperforms the state-of-the-art in both visual quality and recognition accuracy.
Integrating augmented reality into inquiry-based learning approach in primary science classrooms

Notwithstanding the advantages of incorporating Augmented Reality (AR) in education, AR’s concrete uses as compared to other technologies are not fully recognised. Moreover, many of the existing studies have neglected to examine the impact of pedagogy and its corresponding instructional models, whilst implementing AR in teaching and learning. In leveraging the affordances of AR, an inquiry-based learning framework, referred to as QIMS, was proposed in this study. A learning package was developed on the topic of plant reproduction for primary 5 students (aged 11–12) based on the QIMS framework. Using a quasi-experimental approach, this study evaluated three conditions (AR and QIMS; QIMS; Non-AR and Non-QIMS) for a series of science lessons in a primary school. 117 students took part in this study. The quantitative results showed that although there was no statistically significant difference in students’ academic performance when AR was used, students’ self-directed learning and creative thinking skills increased significantly after partaking in the QIMS inquiry-based lessons. The usage of AR and QIMS had a significant effect in increasing students’ critical thinking and knowledge creation efficacy skills. Moreover, in view of students’ academic outcomes, the integration of QIMS and AR proved to be more beneficial to low-progress students. Qualitative analysis of the interview data from teachers and students aids in accounting for the quantitative results and indicate productive implementation strategies. The findings of this study will guide the design of future AR interventions, by providing insights for both researchers and practitioners on how to integrate and implement AR with pedagogical approaches.
Building an Effective Classifier for Phishing Web Pages Detection: A Quantum-Inspired Biomimetic Paradigm Suitable for Big Data Analytics of Cyber Attacks

To combat malicious domains, which serve as a key platform for a wide range of attacks, domain name service (DNS) data provide rich traces of Internet activities and are a powerful resource. This paper presents new research that proposes a model for finding malicious domains by passively analyzing DNS data. The proposed model builds a real-time, accurate, middleweight, and fast classifier by combining a genetic algorithm for selecting DNS data features with a two-step quantum ant colony optimization (QABC) algorithm for classification. The modified two-step QABC classifier uses K-means instead of random initialization to place food sources. In order to overcome ABCs poor exploitation abilities and its convergence speed, this paper utilizes the metaheuristic QABC algorithm for global optimization problems inspired by quantum physics concepts. The use of the Hadoop framework and a hybrid machine learning approach (K-mean and QABC) to deal with the large size of uniform resource locator (URL) data is one of the main contributions of this paper. The major point is that blacklists, heavyweight classifiers (those that use more features), and lightweight classifiers (those that use fewer features and consume the features from the browser) may all be improved with the use of the suggested machine learning method. The results showed that the suggested model could work with more than 96.6% accuracy for more than 10 million query–answer pairs.
EEvoU-Net: An ensemble of evolutionary deep fully convolutional neural networks for medical image segmentation

Developing a Deep Convolutional Neural Network (DCNN) is a challenging research topic which needs extensive efforts and computation to find a proper network structure and a precise set of hyper-parameters. The problem is that in most of the cases the achieved network works well on the specific application or dataset, and a small to significant changes are required to adapt it for a new one. Besides, the limited number of available labelled images and the required computational infrastructure, make this task even more challenging. Therefore, developing an automatic method that is able to find a network structure and its parameters, while using minimum computation, seems necessary. Evolutionary computation is an optimisation method that can be used to address the mentioned difficulties. This paper proposes an evolutionary-based framework to find a set of precise and small networks for medical image segmentation, and also, an ensemble model to improve the quality of segmentation. To the best of our knowledge, EEvoU-Net is the first ensemble method that utilises a set of evolutionary U–Net–based deep networks for medical image segmentation. In the proposed model, a Genetic Algorithm (GA) is applied to design a set of optimal network structures, along with their parameters, using a new fixed-length encoding strategy to create variable length networks, for medical image segmentation. The proposed model is evaluated using five different, publicly available medical image segmentation datasets. The best found evolutionary networks, outperformed U-Net, ResU-Net, DenseU-Net, NAS U-Net, AdaResU-Net, EvoU-Net, DenseRes, 2D to 3D EvoU-Net, and Attention EvoU-Net in the most of the cases using considerable less trainable parameters. Furthermore, EEvoU-Net as an ensemble of evolutionary networks, has also substantially improved over those previous results.