Reviewerly/exHarmony huggingface dataset:
Attribute‐based encryption scheme for secure data sharing in cloud with fine‐grained revocation

Abstract Attribute‐based encryption (ABE) is a prominent cryptographic tool for secure data sharing in the cloud because it can be used to enforce very expressive and fine‐grained access control on outsourced data. The revocation in ABE remains a challenging problem as most of the revocation techniques available today, suffer from the collusion attack. The revocable ABE schemes which are collusion resistant require a semi‐trusted manager to update the secret keys of nonrevoked users in order to achieve revocation. This introduces computation and communication overhead, and also increases the overall security vulnerability. In this paper, we propose two collusion resistant revocable ABE schemes that do not require any semi‐trusted entity. Our first scheme supports revocation at the user‐level that is equivalent to revoking all the attributes from a user. Our second scheme supports revocation at the attribute‐level that enables more fine‐grained revocation by allowing selective attribute(s) revocation from a user. We call them user‐level revocable ABE (ULR‐ABE) and attribute‐level revocable ABE (ALR‐ABE), respectively. For both the schemes, the secret keys of the nonrevoked users are never affected and the decryption algorithm has the same performance as the baseline ABE scheme. We are able to achieve these at the cost of some increase (compared to the baseline scheme) in the size of the secret key and the ciphertext.
How to Marry the Kernel and the Image

SummaryThis note gives a general sufficient and necessary condition to obtain the direct sum E=kerf⊕Imf, where E is a finite dimensional space over the field K (R or C) and f is a linear transformation of E.
Fostering Integrated Governance Quality through Technology Penetration: Thresholds of Democracy in Sub-Saharan Africa

In the quest for the attainment of democracy with its fully unleased potential, the role of information and communication technology (ICT) is integral within this current knowledge economy disposition. The study explores the effect of mobile technology penetration on governance quality from the unconditional and marginal effects of mobile phones and diverse democracy indicators. The analysis is carried out by applying the instrumental variables (IV) Tobit regression to the data to examine the relationship among the variables of interest with a view to handling possible endogeneity issues in the empirical model. The study finds that weak democracy is detrimental to the effect of mobile phone penetration on integrated governance quality and that the higher the mobile phone penetration, the lower the weak democracy quality in SSA. The study concludes by recommending efforts and policies to be enacted and implemented such as the enhancement of mobile technology for concise quality governance. • A weak democratic system decreases the relevance of mobile technology in the SSA region. • Higher mobile phone penetration implies improved democracy quality in SSA. • Thresholds exist that should not be exceeded for the various weak democracy classifications. • Weak liberal democracy’s threshold is higher than weak deliberative democracy’s threshold. • Weak deliberative democracy’s threshold is higher than weak electoral democracy’s threshold.
AADH-YOLOv5: improved YOLOv5 based on adaptive activate decoupled head for garbage detection

As the issue of garbage pollution becomes increasingly urgent, accurate and effective garbage detection can play a crucial role in minimizing environmental pollution and facilitating the conversion of waste into energy. However, the accuracy of current waste detection algorithms is limited. We propose an approach called the adaptive activate decoupled head for YOLOv5 (AADH-YOLOv5) for detecting garbage. Our model improves localization accuracy without significantly increasing floating point of operations (FLOPs) or parameters. Extensive testing on the newly created domestic garbage dataset-6 demonstrates that AADH-YOLOv5 outperforms YOLOv5l and Cascade R-CNN, achieving a 5.4% and 4.2% improvement in mAP@0.5, respectively. Our study also contributes to the field by providing a new dataset for garbage detection research.
Strengthening Cloud Security: A Comprehensive Review of Modern Cryptography Methods and Emerging Trends

To protect data and communication in cloud environments, this article examines the foundations, methods, and protocols of cryptography in the context of cloud computing. Asymmetric and symmetric key cryptography is introduced, with a focus on the difficulties in key distribution and the significance of strong cryptographic algorithms. The paper discusses several cloud cryptography strategies, demonstrating their value in enabling safe data sharing and compute outsourcing while maintaining secrecy. These techniques covered include homomorphic encryption, proxy re-encryption, attribute-based encryption, and searchable encryption. The relevance of cryptographic protocols for cloud security in terms of authentication, access control, and data sharing is examined. Examples include SSL/TLS for secure communication and KMIP for key management. Furthermore, the paper highlights privacy-preserving computation techniques and the usage of cryptographic standards and frameworks provided by NIST, CSA, and TCG in ensuring secure cryptographic practices. The paper concludes by addressing current challenges and future directions, including performance optimization, trust in cloud providers, and post-quantum cryptography, to enhance the security of data in cloud computing
Hyperparameter optimization method based on dynamic Bayesian with sliding balance mechanism in neural network for cloud computing

Abstract Hyperparameter optimization (HPO) of deep neural networks plays an important role of performance and efficiency of detection networks. Especially for cloud computing, automatic HPO can greatly reduce the network deployment cost by taking advantage of the computing power. Benefiting from its global-optimal search ability and simple requirements, Bayesian optimization has become the mainstream optimization method in recent years. However, in a non-ideal environment, Bayesian method still suffers from the following shortcomings: (1) when search resource is limited, it can only achieve inferior suboptimal results; (2) the acquisition mechanism cannot effectively balance the exploration of parameter space and the exploitation of historical data in different search stages. In this paper, we focused on the limited resources and big data provided by the cloud computing platform, took the anchor boxes of target detection networks as the research object, employed search resource as a restraint condition, and designed a dynamic Bayesian HPO method based on sliding balance mechanism. The dynamism of our method is mainly reflected in two aspects: (1) A dynamic evaluation model is proposed which uses the cross-validation mechanism to evaluate the surrogate model library and select the best model in real time; (2) A sliding balance mechanism is designed based on resource constraints to seek a balance between exploration and exploitation. We firstly augment the recommended samples of probability of improvement acquisition function by using k-nearest neighbor method, then introduce Hausdorff distance to measure the exploration value and match sampling strategy with resource utilization, which makes it slide smoothly with resource consumption to establish a dynamic balance of exploration to exploitation. The provided experiments show that our method can quickly and stably obtain better results under the same resource constraints compared with mature methods like BOHB. Graphical Abstract
Organisational culture and information security awareness of Chinese grassroots civil servants: A mediated moderation model

Abstract Digital information and data significantly improve the efficiency of government work in China. At the same time, in order to deal with information security and data leakage events, public departments have begun to pay attention to the information security awareness and behaviour of civil servants. Based on organisational culture and protection motivation theory, this study constructed a mediated moderation model to explore the influence mechanism of grassroots civil servants’ awareness of information security. This study collected 324 questionnaires from four regions of China. Results show that organisational culture has a significant positive impact on information security awareness. Professional identity has a significant positive effect on public service motivation and plays a negative moderating role in the relationship between organisational culture and information security awareness. Public service motivation mediates the moderating effect of professional identity on the relationship between organisational culture and information security awareness. The findings of this study offer practical implications and suggestions for further research. Points for practitioners The construction of organisational culture will effectively enhance the information security awareness of Chinese grassroots civil servants. Public departments should consider professional identity as an important factor when selecting grassroots civil servants engaged in information security work. In the implementation of information security management strategy planning, government departments can pay attention to and emphasise the public service motivation of grassroots civil servants.
Chess Equilibrium Puzzles

SummaryWhat happens when the only goal in a chess game is to capture at least one piece of the opposite side? Can both sides live peacefully in an equilibrium where neither can capture the other’s pieces? In this short paper, we develop a new set of puzzles which we call chess equilibrium puzzles on this premise. We explain the rules of the game, analyze puzzles that have obvious and generalizable solutions, and provide several interesting puzzles for the reader to solve (solutions are provided at the end). Our puzzles provide an exciting twist to the realm of traditional chess puzzles.
Improved wavelet prediction superresolution reconstruction based on U‐Net

Abstract Deep learning can be used to achieve single‐image superresolution (SR) reconstruction. To address problems encountered during this process, such as the number of network parameters, high training requirements on equipment performance, and inability to downsample certain SR images accurately, an image SR reconstruction algorithm based on deep residual network optimization is proposed. The model introduces wavelet transforms based on the original U‐Net, where the U‐Net is trained to obtain SR wavelet feature images at multiple scales simultaneously. This approach reduces the mapping space for the network to learn low‐ to high‐resolution image mapping, which in turn reduces the training difficulty of the model. In terms of network details, the inverse wavelet transform is used in image upsampling to enhance the sparsity of the reconstruction layer in the original network. The network structure of the U‐Net upsampling is adjusted slightly to enable the network to distinguish wavelet images from feature images, thereby improving the richness of the features extracted by the model. The experimental results show that the peak signal‐to‐noise ratio (PSNR) of the fourfold SR model is 32.35 and 28.68 on the Set5 and Set14 validation sets, respectively. Compared with networks that use wavelet prediction mechanisms, such as the deep wavelet prediction SR (DWSR) and deep wavelet prediction‐based residual SR (DWRSR) models, the PSNR for all the tested public datasets is improved by 0.5. The method yields superior results in terms of both visual effect and PSNR, demonstrating the feasibility of the wavelet prediction mechanism in SR reconstruction and thus offering application value and research significance.
Fusion of Bi-GRU and temporal CNN for biomedical question classification

Medical question classification is a crucial step in developing a highly effective question-answering system for the medical field. Accurate classification of questions plays a vital role in selecting appropriate documents for answering those questions. Deep learning models, known for their ability to uncover hidden features, have gained popularity in various natural language processing (NLP) tasks. In this study, we focus on the significance of the Temporal CNN (TCN) model in extracting insightful features from biomedical questions. We propose a novel deep learning model called Bi-GRU-TCN, which combines the advantages of Bi-GRU and TCN. This model not only captures contextual features from the Bi-GRU model but also learns spatial features through TCN layers. Through a series of experiments, we evaluate our proposed approach on benchmark datasets (BioASQ 7b and 8b) using seven deep learning models, including two ensembled models. The results demonstrate that our approach shows outstanding performance in biomedical question classification, as measured by the precision, recall, F-score, and accuracy parameters.
Navigating Technological Obsolescence: Analysis and Reconstruction of Stockhausen’s <i>Mikrophonie I</i>

This article outlines analytical methods for preparing and interpreting Karlheinz Stockhausen’s sextet Mikrophonie I (1964) for tam-tam and electronics. Would-be performers of this work face significant accessibility issues: Stockhausen’s recommended equipment—especially the electronic filter used to process the tam-tam’s sound—is extremely rare. These issues often necessitate performers finding alternative solutions for equipment. Mikrophonie I is one of several works for live electronics that have become increasingly difficult to program, due to the obsolescence of the technology required to perform them. Performers often reconstruct the necessary electronics digitally, using software such as Max/MSP. Wetzel [(2006). “A Model for the Conservation of Interactive Electroacoustic Repertoire: Analysis, Reconstruction, and Performance in the Face of Technological Obsolescence.” Organised Sound 11 (3): 273–284. https://doi.org/10.1017/S1355771806001555] describes a three-stage model for this reconstructive process that foregrounds the need for performer-led analysis. Using Mikrophonie I as a case study, I expand on Wetzel’s model to navigate the reconstruction through two main analytical perspectives: the prioritisation of sound or process. These methods are then applied to my realisation process of Mikrophonie I. I describe the process of constructing a digital filter in Max/MSP based on a patch created by Christopher Burns [(2002). “Realizing Lucier and Stockhausen: Case Studies in the Performance Practice of Electroacoustic Music.” Journal of New Music Research 31 (1): 59–68. https://doi.org/10.1076/jnmr.31.1.59.8104] and compare different interface options for using the filter in performance. Referring to previous recordings by the Stockhausen Ensemble (1965) and the percussion ensemble red fish blue fish (2014), I show how creative interpretations can help ensembles overcome the perceived shortcomings of their available tam-tam. Beyond the specifics of reconstructing the technology required for performing Mikrophonie I, this article underlines the indispensability of analysis for performers who specialise in works with obsolescent technology.
Multipartite entanglement detection via correlation minor norm

Entanglement is a uniquely quantum resource giving rise to many quantum technologies. It is therefore important to detect and characterize entangled states, but this is known to be a challenging task, especially for multipartite mixed states. The correlation minor norm (CMN) was recently suggested as a bi-partite entanglement detector employing bounds on the quantum correlation matrix. In this paper, we explore generalizations of the CMN to multipartite systems based on matricizations of the correlation tensor. It is shown that the CMN is able to detect and differentiate classes of multipartite entangled states. We further analyze the correlations within the reduced density matrices and show their significance for entanglement detection. Finally, we employ matricizations of the correlation tensor for introducing a measure of global quantum discord.
Applying particle swarm optimization-based dynamic adaptive hyperlink evaluation to focused crawler for meteorological disasters

Abstract Traditional semantic-based focused crawlers calculate the topical priority of hyperlink by linearly integrating topical similarity evaluation metrics and empirical weights. However, the manually pre-determined weights may introduce bias in evaluating hyperlinks, resulting in topic deviation during crawling. To address this problem, we propose a dynamic adaptive procedure based on particle swarm optimization which dynamically updates weights in every crawling step and put forward a new focused crawler, called FCPSO. In FCPSO, we utilize domain ontology for topic representation and a comprehensive priority evaluation method to evaluate the topical priority of hyperlink. Furthermore, we construct a multi-objective optimization model for hyperlink selection, in which the strategy of the non-dominant sorting with the nearest farthest candidate solution is proposed to select Pareto-optimal hyperlinks and guide the crawling direction. Extensive experiments demonstrate the effectiveness of FCPSO over other strategies that it can obtain more topic-relevant webpages with less time consumption.
Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

Abstract This paper addresses the integrated tracking and identification problem of a manoeuvring reentry target that performs intentional lateral manoeuvres to disrupt ground radars. Unlike previous approaches, prior knowledge of the lift‐induced drag is incorporated into a new manoeuvring model to describe the reentry target dynamics more explicitly. This model can account for the constraint between lift and drag, which is beneficial in ensuring the reliability of target state estimation. Noticing that the lift‐induced drag is an inherent characteristics of a reentry target that distinguishes the target's identity from others belonging to the same class, the integrated target tracking and identification problem is formulated within the framework of the multiple hypothesis testing about a set of manoeuvring models constructed by different prior knowledge. The proposed approach enables the authors to derive the optimal solution to the given problem in a mathematically rigorous manner. To cope with the real‐time implementation issue, a hypothesis merging strategy is also devised in view of maintaining the target identification performance. Simulation results demonstrate that the proposed scheme provides superior performance and reliability both in target tracking and identification compared to the existing method, despite imperfectness of prior knowledge.
Smooth Operators for Effective Systematic Review Queries

Effective queries are crucial to minimising the time and cost of medical systematic reviews, as all retrieved documents must be judged for relevance. Boolean queries, developed by expert librarians, are the standard for systematic reviews. They guarantee reproducible and verifiable retrieval and more control than free-text queries. However, the result sets of Boolean queries are unranked and difficult to control due to the strict Boolean operators. We address these problems in a single unified retrieval model by formulating a class of smooth operators that are compatible with and extend existing Boolean operators. Our smooth operators overcome several shortcomings of previous extensions of the Boolean retrieval model. In particular, our operators are independent of the underlying ranking function, so that exact-match and large language model rankers can be combined in the same query. We found that replacing Boolean operators with equivalent or similar smooth operators often improves the effectiveness of queries. Their properties make tuning a query to precision or recall intuitive and allow greater control over how documents are retrieved. This additional control leads to more effective queries and reduces the cost of systematic reviews.
Analysis of the Influence of Distance Learning Through E-Learning and Digital Technology Literacy Ability on Student Learning Achievement with Self-Efficacy Mediation

This study aims to analyze the effect of distance learning through e-learning and digital technology literacy skills on student achievement by mediating self-efficacy. This study used explanatory research with a total sample of 106 STMIK Bina Patria students. Sampling was carried out using a simple random sampling method. The data analysis technique for testing the hypothesis uses path analysis to see the direct and indirect effects of distance learning variables using e-learning, digital literacy, and self- efficacy as mediators on learning achievement. The results of this study are: (1) Utilization of e-learning (X1) has no effect on student self-efficacy (M); (2) Digital literacy (X2) has a positive and significant effect on student self-efficacy (M); (3) Utilization_of e-learning (X1) has a positive and significant effect on learning achievement in practicum courses (Y); (4) Digital literacy (X2) has no effect on learning achievement in practicum courses (Y); (5) student self-efficacy (M) has a positive and significant effect on learning achievement in practicum courses (Y); (6) Based on the Sobel test, the t count &gt; 1.96 is obtained, which means that the variable student self-efficacy (M) mediates the utilization of e-learning (X1) and digital literacy (X2) on learning achievement in practicum courses (Y) ; (7) Based on the Sobel test, the t count &gt; 1.96 is obtained, which means that Student Self Efficacy (M) mediates the influence of Digital LiteracyDirecting (X2) terhadap Learning Achievement in Practicum courses (Y).
RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure

Recently, the broad learning system (BLS) has been widely developed in virtue of its excellent performance and high-computational efficiency. However, two deficiencies still exist in BLS and preclude its deployment in real applications. First, the standard BLS performs poorly in outlier environments because the least squares loss function it uses to train the network is sensitive to outliers. Second, the model structure of BLS is likely to be redundant since the hidden nodes in it are randomly generated. To address the above two issues, a new robust and compact BLS (RCBLS), based on M-estimator and sparsity regularization, is proposed in this paper. The RCBLS develops from the BLS model and maintains its excellent characteristics, but replaces the conventional least squares learning criterion with an M-estimator-based loss function that is less sensitive to outliers, in order to suppress the incorrect feedback of the model to outlier samples, and hence enhance its robustness in the presence of outliers. Meanwhile, the RCBLS imposes the sparsity-promoting l2,1 -norm regularization instead of the common l2-norm regularization for model reduction. With the help of the row sparsity of l2,1-norm regularization, the unnecessary hidden nodes in RCBLS can be effectively picked out and removed from the network, thereby resulting in a more compact network. The theoretical analyses on outlier robustness, structural compactness and computational complexity of the proposed RCBLS model are provided. Finally, the validity of the RCBLS is verified by regression, time series prediction and image classification tasks. The experimental results demonstrate that the proposed RCBLS has stronger anti-outlier ability and more compact network structure than BLS and other representative algorithms.