Reviewerly/exHarmony huggingface dataset:
A density connection weight-based clustering approach for dataset with density-sparse region

Density-Based clustering, an important clustering analysis approach, has several advantages of discovering clusters with arbitrary shapes and identifying noise samples etc. However, existing density-based clustering approaches cannot effectively identify clusters in density-sparse region. In this paper, we propose a density-based clustering approach for dataset with density-sparse region by using density connection weight. Firstly, we define a series of new concepts, such as density connection weight and average weight. And data objects are divided into core objects, boundary objects and noise objects according to average weight, which can effectively distinguish core objects and boundary objects in density-sparse region. Secondly, a density clustering assignment strategy is constructed based on density connection weight. A density clustering algorithm, together with the dimensionality reduction using t-SNE, judiciously handles the low-density clusters in multi-density datasets and the minimal clusters in extremely imbalanced datasets. In the end, the experimental results on the UCI and synthetic datasets validate that our algorithm exhibits prominent clustering performance, especially while being adapted in diverse clustering tasks, including the multi-density, imbalanced and uniform distribution datasets.
Application of integrated geological and structural investigations to the discovery of new mineral zones related to the Daping gold deposit, Southwest China

The Daping gold deposit is one of the largest gold deposits in the Ailaoshan gold belt of SW China. It has proven gold reserves of more than 70 tonnes and has been mined for 36 years. Finding new orebodies to extend the mine life has become urgent since the main gold orebodies are almost depleted. Ore deposit types in the Daping area include quartz-vein and porphyry gold types of mineralization. The quartz-vein ores are hosted in late Proterozoic diorite and Silurian limestones and controlled by NW trending faults. We have studied the ore-controlling structures to determine palaeo-stress directions of V1-2-3 and V37 orebodies, and the distribution of gold mineralization in the V1-2-3 orebodies. Based on the field observation, we identified six stages of structure in the Daping gold deposit, i.e., pre-mineralization (stage 1), syn-mineralization (stages 2 to 4), and post-mineralization (stages 5 and 6). The spatial distribution of gold orebodies at Daping is mainly controlled by the compressional faults of stage 1 that is associated with the Ailaoshan orogenic belt, and overprinted by structures of later stages. Orebodies from gold deposits in orogenic belts commonly have greater vertical dimensions than horizontal dimensions, suggesting that V1-2-3 orebodies may extend to deeper area. Our data indicate that higher gold grades and the thickest parts of the orebodies are associated with deeper parts of the V1-2-3 orebodies (up to 800 m in depth), and are likely related to a concealed mineralized porphyry which was intersected in drill core and identified as xenolith in lamprophyre dikes in the underground mine workings. The quartz-vein type gold mineralization at the Daping gold deposit was possibly overprinted by the porphyry gold mineralization, as indicated by the discovery of the new quartz-vein-associated orebodies beneath the current levels of Daping gold deposit and associated porphyry gold orebodies at Daping gold area.
Autoencoder-based Data Augmentation for Deepfake Detection

Image generation has seen huge leaps in the last few years. Less than 10 years ago we could not generate accurate images using deep learning at all, and now it is almost impossible for the average person to distinguish a real image from a generated one. In spite of the fact that image generation has some amazing use cases, it can also be used with ill intent. As an example, deepfakes have become more and more indistinguishable from real pictures and that poses a real threat to society. It is important for us to be vigilant and active against deepfakes, to ensure that the false information spread is kept under control. In this context, the need for good deepfake detectors feels more and more urgent. There is a constant battle between deepfake generators and deepfake detection algorithms, each one evolving at a rapid pace. But, there is a big problem with deepfake detectors: they can only be trained on so many data points and images generated by specific architectures. Therefore, while we can detect deepfakes on certain datasets with near 100% accuracy, it is sometimes very hard to generalize and catch all real-world instances. Our proposed solution is a way to augment deepfake detection datasets using deep learning architectures, such as Autoencoders or U-Net. We show that augmenting deepfake detection datasets using deep learning improves generalization to other datasets. We test our algorithm using multiple architectures, with experimental validation being carried out on state-of-the-art datasets like CelebDF and DFDC Preview. The framework we propose can give flexibility to any model, helping to generalize to unseen datasets and manipulations.
Further investigations on permutation based constructions of bent functions

Constructing bent functions by composing a Boolean function with a permutation was introduced by Hou and Langevin in 1997. The approach appears simple but heavily depends on the construction of desirable permutations. In this paper, we further study this approach by investigating the exponential sums of certain monomials and permutations. We propose several classes of bent functions from quadratic permutations and permutations with (generalized) Niho exponents, and also a class of bent functions from a generalization of the Maiorana-McFarland class. The relations among the proposed bent functions and the known families of bent function are studied. Numerical results show that our constructions include bent functions that are not contained in the completed Maiorana-McFarland class M#, the class PSap or the class H.
Education in times of digital culture: opportunities and challenges

Education has been treated throughout its history as a space for experiences and innovations. In a time of digital culture, cyberspace and cyberculture are inescapable spaces, generating in educators and students the deterministic notion that there are no alternatives. Between apocalyptic and integrated, this article reflects on the role of digital culture in the educational field, trying to show that it is still possible to preserve values and transmit them in a moment of great and rapid transformations. Learning the dominant language (digital culture) is essential to change the system from within. This learning can create new worlds and new opportunities for educators and students.
GMF-GAN: Gradual multi-granularity semantic fusion GAN for text-to-image synthesis

Text-to-image Synthesis is to obtain images with both authenticity and semantic consistency from natural language descriptions. However, in previous methods, the fusion of semantic information during image generation is insufficient. The multi-stage model fuses semantic information of different granularities in different stages, so the semantic information is not fully utilized. The single-stage model only aims at fusing coarse-grained semantic information, ignoring the detailed information in fine-grained semantics. To this end, we propose a gradual multi-granularity semantic fusion GAN, which gradually fuses multi-granularity semantic information into feature maps of different scales and realizes the full utilization of multi-granularity semantic information. GMF-GAN adopts two novel attention modules: Adaptive Sentence Attention Fusion (ASAF) module and Adaptive Word Attention Fusion (AWAF) module. The ASAF guides the generator to focus on sentence-related regional features, and adaptively fine-tunes feature maps via attention weights. The AWAF assigns greater weight to word-related regional features by modeling the importance of each word. We also propose a text-image consistency loss to ensure that the generated image is semantically consistent with the input text. Qualitative and quantitative experimental results on two benchmark datasets show that GMF-GAN outperforms the state-of-the-art models.
The Mango Model: Best Practices in the Creation of a COVID-19 Open Data Project Through a Partnership with Google Health and the Non-Profit FinMango

This article addresses the issue of accessibility related to collecting and publishing Open Data for COVID-19, and suggestions for using the finalized project data in addressing lifestyle behaviors that will inform Health Educators in differing agencies when developing programs for prevention and management of COVID-19. The Mango Model, based on evidence from a COVID-19 Open Data Project was funded by Google Health in partnership with the nonprofit FinMango. First, the purpose and background for the creation of the Open Data Project are provided. Second, outreach to various agencies is addressed. Third, differing methodologies used by agencies for collecting, organizing, automating, and publishing subnational were observed. Fourth, implications for organizations regarding practice, policy and research are discussed. The Google Health and FinMango COVID-19 Open Data Project reinforces the need for a universal standard for publishing open data with consistent reporting. Through the creation of the project, the Mango-Model offers best practices and solutions for universally releasing types of data to assist public health professionals, researchers, policymakers, and others in health-related fields in understanding and managing the virus, and in the mobilization of resources in future responses to local and global need.
A System Dynamics Approach to Evaluate Advanced Persistent Threat Vectors

Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage by mitigating specific socio-technical cues. This study provides valuable insights into the dynamics of cyber threats, making it the first to apply SD to APTs.
The Application and Ethics of Artificial Intelligence in Blockchain

AI-enabled blockchain refers to the use of AI to enable the analysis and decision-making processes based on data collected, shared, and stored by blockchain. This helps overcome some of the existing challenges in blockchain applications. Despite the growing number of review papers on blockchain and AI, there is a dearth of literature on AI-enabled blockchain in business scenarios. This study uses bibliometric-content analysis to (1) identify three stages of development of AI-enabled blockchain literature and point out the increasing diversity of technological applications; (2) identify the strongest foci of extant literature; (3) unveil the roles of AI-enabled blockchain in 10 application sectors, and identify the key roles of AI in enabling blockchain applications; (4) conclude the referred ethical issues from three levels and make further discussion. The findings present the trends of AI-enabled blockchain and could help developers and service providers better manage the use and ethical issues of AI in blockchain applications.
Multilabel Classifier Chains Algorithm Based on Maximum Spanning Tree and Directed Acyclic Graph

The classifier chains algorithm is aimed at solving the multilabel classification problem by composing the labels into a randomized label order. The classification effect of this algorithm depends heavily on whether the label order is optimal. To obtain a better label ordering, the authors propose a multilabel classifier chains algorithm based on a maximum spanning tree and a directed acyclic graph. The algorithm first uses Pearson's correlation coefficient to calculate the correlation between labels and constructs the maximum spanning tree of labels, then calculates the mutual decision difficulty between labels to transform the maximum spanning tree into a directed acyclic graph, and it uses topological ranking to output the optimized label ordering. Finally, the authors use the classifier chains algorithm to train and predict against this label ordering. Experimental comparisons were conducted between the proposed algorithm and other related algorithms on seven datasets, and the proposed algorithm ranked first and second in six evaluation metrics, accounting for 76.2% and 16.7%, respectively. The experimental results demonstrated the effectiveness of the proposed algorithm and affirmed its contribution in exploring and utilizing label-related information.
Teaching Practice Supervisors' Experiences of Being Involved in Mobile App Development at an ODe-L Institution

This study used a case study to map teaching practice supervisors' experiences of being involved in developing the teaching practice mobile app (TPMA) for supervising students at an open distance e-learning (ODeL) institution. The technology acceptance model (TAM) was used as a theoretical framework for the study. Data were collected through semi structured interviews and analyzed thematically. The perceived usefulness of participating in app development is associated with empowerment and the opportunity to reflect. The participants' perceived ease of use was unfavorable, as they found downloading the app challenging and received minimal support from the developer. Even at an ODeL institution, supervisors would typically travel to visit students in teaching practice. The teaching practice mobile app would give supervisors flexibility and access to their students, as they would no longer have to travel to schools, and they could supervise their students remotely. The study recommends support for university supervisors to integrate mobile technologies into their teaching practice.
GFNER: A Unified Global Feature-Aware Framework for Flat and Nested Named Entity Recognition

Named Entity Recognition (NER) poses challenges for both flat and nested tasks, which require different paradigms. To overcome this issue, we present GFNER, a unified global feature-aware framework based on table filling, that can handle both types of tasks with low computational cost. While pretrained models have shown great promise in NER, they typically focus on local contextual information, disregarding global relationships that are crucial for accurate entity boundary extraction. To address this limitation, we introduce a global feature learning module that captures the inter-entity associations and significantly enhances entity recognition. Experimental results on flat and nested NER datasets demonstrate that GFNER outperforms previous state-of-the-art models. The code for GFNER is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cjymz886/GFNER</uri> .
The Distributions of Uncalibrated Speaker Verification Scores: A Generative Model for Domain Mismatch and Trial-Dependent Calibration

Speaker verification systems that compute log-likelihood ratios (LLR) between the same and different speaker hypotheses allow for cost-effective decisions that depend only on prior information. Domain mismatch, inaccurate model assumptions or the intrinsic nature of non-probabilistic classifiers often result in mis-calibrated scores, and a re-calibration step is required to map the classifier outputs to well-calibrated LLRs. Standard calibration is based on Logistic Regression, often paired with quality measures to provide trial-dependent calibration transformations. More recently, generative methods have been proposed as an alternative to discriminative approaches, which, however, are not yet able to exploit additional side information. In this work we introduce a novel generative approach based on the analysis of the effects of speaker vector distribution mismatch on the distribution of verification scores for PLDA and PLDA-based classifiers. We show that target and non-target scores can be modeled by Variance-Gamma distributions, whose parameters represent effective between and within-class variability. This allows us to introduce utterance-dependent variability models that can incorporate both explicit quality measures, such as the utterance duration, or implicit measures, such as the norm of a speaker embedding. Experimental results on different test sets with different front-ends and classifiers show that the proposed approach improves both calibration and verification accuracy with respect to state-of-the-art calibration models.
An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE

Providing end-to-end security is vital for most networks. Emerging quantum computers make it necessary to design secure crypto-systems against quantum attacks. Binary Ring Learning With Error (Ring-Bin LWE) is a Lattice-based cryptography that is hard to solve by quantum computers. Also, this algorithm does not have costly operations in terms of area, making Ring-Bin LWE a suitable algorithm for resource-constraint devices. This work presents a lightweight hardware implementation of Ring-Bin LWE. In the proposed design, a new multiplication method and design for Ring-Bin LWE is introduced which results in latency reduction by a factor of two. Using column-based multiplication, our design processes two consecutive coefficients in each cycle. The architecture is designed based on the proposed multiplication and contains one specific register bank with two sub-bank registers. The design is implemented on the FPGA platforms. The implementation results show an impressive improvement in execution time and Area-Time metrics over previous similar works.
Impact of the Global University Ranking Systems on Higher Education in Ghana With Special Focus on the Role of Academic Libraries

University rankings have altered the landscape of higher education around the world, and it is expected to continue to affect higher education institutions (HEI). The objective of this book chapter was to explore the implications of global university ranking systems on higher education in Ghana. Three ranking systems; times higher education (THE) world university rankings, webometrics ranking, and quacqarelli-symonds (QS) rankings in which the University of Ghana features were reviewed. Further, this book chapter discusses the impact that ranking systems have on higher education and its stakeholders. The study established that collaboration between the faculty and libraries is critical to ensuring higher scores by the ranking systems. This book chapter concludes that HEI in Ghana need to reposition libraries to better support universities in enhancing the visibility and ranking of the university globally. Finally, the authors have provided specific local solutions to be adopted by stakeholders in HEI.
Research Performance and Ranking of South African Universities Using Altmetric Data

This article used Altmetrics data extracted from Altmetric.com to rank twenty-two public universities in South Africa that yielded data in the database, as well as to compare their altmetrics and webometrics rankings. Descriptive statistics and correlation analyses were conducted to uncover the universities' ranking patterns across several indicators and sources. The findings reveal that the historically advantaged universities that have continued to dominate the global ranking systems performed better than the historically disadvantaged universities. There were strong and positively significant relationships between altmetrics and the citation-based rankings on the one hand, and the webometrics rankings on the other hand – albeit a few instances of weak relationships between the variables. The authors propose the consideration of altmetrics as alternative impact indicators in the global rankings systems in an attempt to achieve a more robust and pluralistic picture.
Detection of solar panel defects based on separable convolution and convolutional block attention module

The share of renewable energy in the electricity market is increasing year by year. It is necessary to identify damage of solar panels in a timely manner, as solar panels are important components in photovoltaic power generation. In this paper, a lightweight solar panel fault diagnosis system based on image pre-processing and an improved VGG-19 network is proposed to address the problem of blurred solar panel field images, which are not easy for defects detection. First, we use Daubechies 4(DB4) wavelet and morphology-based enhancements to improve the quality of solar panel images. Then, conventional convolutional layers in the VGG-19 are replaced with separable convolutional layers to reduce the number of network parameters and improve training efficiency. Finally, the Convolutional Block Attention Module (CBAM) is introduced to improve the accuracy of solar panel defects’ detection. A dataset consisting of 3344 images of solar panels was used to evaluate the performance of the proposed method in defect detection. The experimental results show that the method has an accuracy of 87.8% and a detection speed of 0.047 s per image. The proposed model has higher accuracy and more stable performance than other conventional networks with a lightweight structure, demonstrating the reliability of the improved VGG-19 in detecting solar panel defects in practical applications.
Public key encryption with public-verifiable decryption delegation and its application

We introduce a new cryptographic primitive called public key encryption with public-verifiable decryption delegation (PKE-PV D2), that enables the original decryptor to transmit the decryption key for a specific ciphertext to a designated recipient in a way that is both public-verifiable and privacy-preserving. Because of its ability to delegate public-verifiable and ciphertext-level decryption, it is useful in scenarios that require special decryption delegation, such as when the court needs to retrieve a specific message from the ciphertext as significant judgment evidence. Moreover, we present the first PKE-PV D2 scheme and its corresponding security proof in the random oracle model, using bilinear pairing. Additionally, we provide a prototype of our proposal, and our extensive experimental results demonstrate its effectiveness and efficiency.