Reviewerly/exHarmony huggingface dataset:
Mining relevant partial periodic pattern of multi-source time series data

Traditional partial periodic pattern mining algorithms tend to work on a single time series or database. However, time series databases usually consist of interrelated multivariate time series from different sources in real application scenarios. A new pattern–relevant partial periodic patterns and its corresponding mining algorithm (PMMS-Eclat) are designed, which can effectively reflect and mine the correlations of multi-source time series data. And PMMS-Eclat introduces the contribution difference of various patterns in the mining process to quickly identify important patterns. Firstly, PMMS-Eclat, an improved mining algorithm based on Ecalt, is used to obtain frequent partial periodic patterns from single time serie, which avoids the expensive tree structure adjustment cost. In this process, two new metrics are defined to characterize the correlation among sequential data effectively, namely density ratio and average periodic rate, which respectively reflect the density and occurrence rate of patterns. Secondly, the frequent partial periodic patterns excavated above are used as input matrices, and the ultimate relevant partial periodic patterns are obtained based on LSH principle. Finally, experiments on various datasets are conducted to verify the effectiveness of our proposed algorithm, and the results show that PMMS-Eclat is more competent for sparse databases.
A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Abstract Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.
Degradation-aware and color-corrected network for underwater image enhancement

Underwater image enhancement has played an important role in marine resource monitoring and preservation. However, the visual quality of underwater images is degraded due to light attenuation, background noise and water types, which brings challenging problems to underwater image enhancement. Existing Convolutional Neural Network (CNN) based methods do not fully explore degradation issues in different underwater scenes and also ignore color corrected priors, which limit their generalization and representation ability. To address the above mentioned problems, we propose a new Degradation-aware and Color-Corrected Network (DCN). Since it is difficult to estimate the degradation information of underwater image, we use Contrastive Learning (CL) to learn accurate degradation representation from input underwater images via a new degradation encoder. Then, to make the enhancement process adapt to different underwater images with the help of learned degradation representation and a color corrected prior, the DCN is built based on the proposed Degradation-aware and Color-corrected Block (DCB) for enhancing underwater images, which contains a degradation-aware branch, a color-corrected branch and an Edge Sharpness Layer (ESL). The degradation-aware branch recovers the clear image with the help of the degradation representation, while the color-corrected branch based on the color corrected prior compensates for the loss of information in the red channel. Furthermore, the ESL based on the unsharp masking principle is proposed and inserted at the end of the DCB for preserving details and sharpening edges. Such a design enables the DCN enhance underwater images, correct colors and sharpen edges simultaneously. The experiments on both synthetic and real images demonstrate that our method achieves pleasing visual results and outperforms state-of-the-art methods by a large margin. • A Degradation-aware and Color-Corrected Network (DCN) which enhances underwater images with different degradation and color deviation is proposed. • The proposed DCN consists two new modules: a new Degradation-aware and Color-Corrected Block (DCB) and a new Edge Sharpness Layer (ESL). • The new DCB is based on degradation representation trained by contrastive learning and a color-corrected prior. • The new ESL is based on the unsharp masking principle.
Multitask Image Splicing Tampering Detection Based on Attention Mechanism

In today’s modern communication society, the authenticity of digital media has never been of such importance as it is now. In this aspect, the reliability of digital images is of paramount importance because images can be easily manipulated by means of sophisticated software, such as Photoshop. Splicing tampering is a commonly used photographic manipulation for modifying images. Detecting splicing tampering remains a challenging task in the area of image forensics. A new multitask model based on attention mechanism, densely connected network, Atrous Spatial Pyramid Pooling (ASPP) and U-Net for locating splicing tampering in an image, AttDAU-Net, was proposed. The proposed AttDAU-Net is basically a U-Net that incorporates the spatial rich model filtering, an attention mechanism, an ASPP module and a multitask learning framework, in order to capture more multi-scale information while enlarging the receptive field and improving the detection precision of image splicing tampering. The experimental results on the datasets of CASIA1 and CASIA2 showed promising performance metrics for the proposed model (F1-scores of 0.7736 and 0.6937, respectively), which were better than other state-of-the-art methods for comparison, demonstrating the feasibility and effectiveness of the proposed AttDAU-Net in locating image splicing tampering.
An analysis on the relationship between ICT, financial development and economic growth: Evidence from Asian developing countries

This study examines the effect of ICT progress on economic growth by constructing an ICT diffusion index. Along with ICT, this study further investigates the role of financial development and the combined impact of ICT and financial development on growth in 10 Asian developing economies covering the period 2001–2017. To analyze the panel data, this study employed the ARDL model and estimated the Pooled Mean Group estimator. The estimated results reveal a significant positive long-run relationship between financial expansion and economic progress. While ICT hurts economic growth on its own, it has a significant positive impact when combined with financial development. The robustness of the results has been verified by Fully-Modified OLS (FMOLS) and Dynamic OLS (DOLS) estimations. To promote enduring economic growth in Asian developing countries, the paper recommends ensuring inclusive financial development combined with modern ICT.
Investigating the Decentralized Governance of Distributed Ledger Infrastructure Implementation in Extended Enterprises

Abstract Distributed ledger technology (DLT) is a disruptive technology with the potential of improving extended enterprise (EE) which comprises organizations that combine their capabilities and knowhow to achieve a common goal. Within the extended enterprise, governance enables distributed ledger control and stewardship and provides effective ways for enforcing ledger access and ownership policies. Although research related to DLT has received attention from academics and industries, the decentralized governance perspective of DLT remains less explored, and it is uncertain how decisions are made concerning the deployment of DLT within EE. These call for governance policies to protect the interests and needs of all stakeholders within the extended enterprise. Therefore, there is need for research that provides insight regarding the decentralized governance of DLT, showing how stakeholders and actors within EE make informed decisions. Accordingly, grounded on academic literature, this study develops governance of DLT framework for extended enterprises which comprises DLT governance dimensions, stakeholder/actors, and DLT governance mechanisms. The findings also present the governance actors and categories of DLTs, potentials, and challenges regarding decentralized governance of DLT in extended enterprises. Findings provide implications to enterprises, researchers, practitioners, industries, and policymakers on the concept of decentralized governance for organizational transformation. Finally, findings from this research are instrumental for designing governance policies to support DLT implementation and stimulate potential research within this direction.
Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation

Supervised segmentation can be costly, particularly in applications of biomedical image analysis where large scale manual annotations from experts are generally too expensive to be available. Semi-supervised segmentation, able to learn from both the labeled and unlabeled images, could be an efficient and effective alternative for such scenarios. In this work, we propose a new formulation based on risk minimization, which makes full use of the unlabeled images. Different from most of the existing approaches which solely explicitly guarantee the minimization of prediction risks from the labeled training images, the new formulation also considers the risks on unlabeled images. Particularly, this is achieved via an unbiased estimator, based on which we develop a general framework for semi-supervised image segmentation. We validate this framework on three medical image segmentation tasks, namely cardiac segmentation on ACDC2017, optic cup and disc segmentation on REFUGE dataset and 3D whole heart segmentation on MM-WHS dataset. Results show that the proposed estimator is effective, and the segmentation method achieves superior performance and demonstrates great potential compared to the other state-of-the-art approaches. Our code and data will be released via https://zmiclab.github.io/projects.html, once the manuscript is accepted for publication.
A systematic review of library makerspaces research

Despite the abundance of research on library makerspaces, systematic reviews of library makerspace research are lacking. As research on library makerspaces advances, the field needs reliable empirical findings to examine the impact of library makerspaces and identify research areas that are valuable for future research. Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, 43 out of 838 records were selected for the systematic review. The overall trend of research methodologies and theories, settings, participants, research purposes, as well as tools, technologies and programming in library makerspace research were identified. The findings reveal that qualitative studies that were descriptive in nature were the predominant approaches. While appropriate literatures were explored, theoretical frameworks were less used. This systematic review contributes new areas and directions for future research, including the need for expansion of research methodologies and theoretical frameworks and investigation of diverse users and types of making.
Cross-view graph matching for incomplete multi-view clustering

Multi-view clustering (MVC) focuses on adaptively partitioning data from diverse sources into the respective groups and has been widely studied under the assumption of complete data. However, real-world applications often encounter a more realistic incomplete multi-view clustering (IMVC) problem, where data samples are missing in certain views. There are two challenges in IMVC: 1) how to reduce the impact of the missing instances; 2) how to effectively extract the consistent information to cluster the multi-view data. To address the challenges, we propose an adaptive graph learning framework for IMVC, which optimizes the missing information to fit the intrinsic structure of each view and clusters the multi-view data by cross-view graph matching. The proposed method mainly consists of three steps. Firstly, owing to the outstanding performance of the intrinsic structure of data, we adapt it to complete the missing data of each view. Secondly, the connection graph of each view from a projection space is adaptively constructed wherein the data points are connected if and only if they belong to the same cluster. Thirdly, we further introduce a cross-view graph matching strategy to appropriately utilize complementary multi-views information and preserve view-specific semantic information. We develop an iterative algorithm for solving the proposed model. Numerical experiments on several standard datasets demonstrate the effectiveness of the proposed method.
Pre-trained Language Model-based Retrieval and Ranking for Web Search

Pre-trained language representation models (PLMs) such as BERT and Enhanced Representation through kNowledge IntEgration (ERNIE) have been integral to achieving recent improvements on various downstream tasks, including information retrieval. However, it is nontrivial to directly utilize these models for the large-scale web search due to the following challenging issues: (1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web document, prohibit their deployments in the web search system that demands extremely low latency; (2) the discrepancy between existing task-agnostic pre-training objectives and the ad hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online retrieval and ranking effectiveness; and (3) to create a significant impact on real-world applications, it also calls for practical solutions to seamlessly interweave the resultant PLM and other components into a cooperative system to serve web-scale data. Accordingly, we contribute a series of successfully applied techniques in tackling these exposed issues in this work when deploying the state-of-the-art Chinese pre-trained language model, i.e., ERNIE, in the online search engine system. We first present novel practices to perform expressive PLM-based semantic retrieval with a flexible poly-interaction scheme and cost-efficiently contextualize and rank web documents with a cheap yet powerful Pyramid-ERNIE architecture. We then endow innovative pre-training and fine-tuning paradigms to explicitly incentivize the query-document relevance modeling in PLM-based retrieval and ranking with the large-scale noisy and biased post-click behavioral data. We also introduce a series of effective strategies to seamlessly interwoven the designed PLM-based models with other conventional components into a cooperative system. Extensive offline and online experimental results show that our proposed techniques are crucial to achieving more effective search performance. We also provide a thorough analysis of our methodology and experimental results.
Reason Based Machine Learning Approach to Detect Bangla Abusive Social Media Comments

For the study issue of abusive language detection, English is the most commonly employed language. There are just a few works accessible in low-resource languages such as Bangla. People use these sorts of statements on many social media sites. As a result, detection of this type of language is a demand of time. Our goal is to identify this abusive Bangla language in a novel approach. There are some works that use Bengali corpus and transliterated Bengali corpus to detect abusive language. However, in this research, we utilized annotated translated Bengali corpora, and we added a formal justification in each remark for being classified as abusive or non abusive language. For evaluations, we employed a variety of machine learning classifiers where logistic regression achieves 97% accuracy.
Single-Shot Three-Dimensional Reconstruction Using Grid Pattern-Based Structured-Light Vision Method

Structured-light vision methods are widely employed for three-dimensional reconstruction. As a typical structured light pattern, grid pattern is extensively applied in single-shot three-dimensional reconstruction. The uniqueness of the grid feature retrieval is critical to the reconstruction. Most methods using grid pattern utilize the epipolar constraint to retrieve the correspondence. However, the low calibration accuracy of the camera–projector stereo system may impact the correspondence retrieval. An approach using grid pattern-based structured-light vision method is proposed. The grid pattern-based structured-light model was combined with the camera model and the multiple light plane equations. An effective extraction method of the grid stripe features was investigated. The system calibration strategy, based on coplanar constraint, is presented. The experimental setup consisted of a camera and an LED projector. Experiments were carried out to verify the accuracy of the proposed method.
Homonuclear Molecules Optimization (HMO) meta-heuristic algorithm

This study introduces a novel meta-heuristic algorithm known as Homonuclear Molecules Optimization (HMO) for optimizing complex and nonlinear problems. HMO is inspired by the arrangement of electrons around atoms given the Bohr atomic model and the structure of homonuclear molecules. This algorithm is based on creating the initial population of a set of atoms in the search space and the electrons associated with each atom (searching agents) given the quantum numbers. In each iteration, the best electron of each atom is selected as the new location of the nucleus, and a number of atoms move toward the atom with the best solution to form a homonuclear molecule. The results of applying the MHO algorithm were evaluated in comparison with three classical optimization algorithms of PSO, GA, and DE along with a novel algorithm called Equilibrium Optimizer (EO). The HMO was able to precisely solve unimodal functions and find global and local solutions for multimodal functions. The outcomes of Wilcoxon’s rank-sum test demonstrated that there is a significant difference between the final results of HMO and those of the other algorithms ( a = 0 .05%). Overall, it was concluded that the HMO outperforms the classical algorithms and can compete with new and efficient algorithms such as EO.
Low-rank constraint based dual projections learning for dimensionality reduction

Subspace learning is a widely-used fundamental method for feature extraction in several fields. Existing subspace-based methods only concentrate on projecting all data into a single subspace to achieve feature extraction. However, there are not only one task of the projection matrix to handle in majority existing methods. And it always need to deal multiple tasks (e.g., dimensional reduction, data similarity preservation, etc.), leading to the over-pressure for the single subspace and degrade the accuracy of the model. In order to deal with this issue, a dual representation locality preserving projection (DRLPP for short) is presented in this paper, in which dual different projection matrices are introduced to better accomplish multiple tasks. Specially, these two projection matrices are relaxed into a flexible form to select appropriate features for preserving the important properties of data. Meanwhile, as the two matrices share the same tasks, the structure of matrices and the corresponding projected data contain intrinsic geometric structure. Then, a structural similarity term and a linear subspace reconstruction term are proposed to deeply capture the potential relationship and maintain a suitable similarity of projected data. Moreover, a low-rank constraint is imposed to preserve the similarity between the two matrices and reduce the noise disturbance. Finally, an iterative algorithm with fast convergence is proposed to solve the corresponding optimization problem. Experimental results on five datasets demonstrate the effectiveness of the proposed method.
SybilFlyover: Heterogeneous graph-based fake account detection model on social networks

Organized social robot accounts can launch Sybil attacks on online social networks (OSNs) for various malicious purposes, thus significantly affecting the user experience of online communities and damaging the reputation of OSNs. Therefore, detecting Sybil accounts in OSNs is crucial in cyberspace governance. Among the existing Sybil account detection methods, structure-based methods appear to be the most promising because of their ability to model the states and behaviors of social network accounts effectively. However, most of these structure-based methods only model the social accounts in OSNs while ignoring the content generated in OSNs, such as tweets of Twitter accounts. To address this deficiency, this study proposes an efficient model called SybilFlyover, which uses a heterogeneous graph-based method to represent all types of entities existing in an OSN uniformly, as well as complex relationships between the entities in a directed heterogeneous social network graph. During the modeling process, content-based social network information is injected into the model using a method based on prompt learning to achieve more accurate modeling of the real state of an OSN. Finally, the social network graph was processed using a transformer-based method to identify nodes representing Sybil accounts. The results of an experiment on a real public dataset demonstrate that the proposed SybilFlyover model outperforms existing state-of-the-art baseline models in Sybil account detection.
Distilling and transferring knowledge via cGAN-generated samples for image classification and regression

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a teacher. However, applying KD in image regression with a scalar response variable has been rarely studied, and there exists no KD method applicable to both classification and regression tasks yet. Moreover, existing KD methods often require a practitioner to carefully select or adjust the teacher and student architectures, making these methods less flexible in practice. To address the above problems in a unified way, we propose a comprehensive KD framework based on cGANs, termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This novel mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. An error bound for a student model trained in the cGAN-KD framework is derived in this work, providing a theory for why cGAN-KD is effective as well as guiding the practical implementation of cGAN-KD. Extensive experiments on CIFAR-100 and ImageNet-100 show that we can combine state of the art KD methods with the cGAN-KD framework to yield a new state of the art. Moreover, experiments on Steering Angle and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable.