Reviewerly/exHarmony huggingface dataset:
Fine-Grained Data Sharing With Enhanced Privacy Protection and Dynamic Users Group Service for the IoV

The Internet of Vehicles (IoV) is expected to play a revolutionary role in improving users’ driving experience and urban traffic governance. By widely absorbing emerging technologies including cloud computing, the future IoV evolution is leading towards providing more flexible and diversified data services. However, the publicly accessible IoV environment arouses the user’s concerns about the leakage of data and personal privacy. Despite some cryptographic solutions have been proposed, they still raise challenges on privacy, efficiency and usability. To cope with these challenges, this paper first presents an efficient scheme PH-ABE-DS, which attains the full policy hiding by implementing the access control with the inner product. Besides, we design an efficient indirect revocation mechanism, to enable the cloud and users to update the ciphertext and user secret key with slight storage and computational overheads. On this basis, we then present the EA-PH-ABE-DS scheme, by resorting to edge computing, it further reduces the overheads of resource-constrained devices. We design a deployment model for EA-PH-ABE-DS in IoV to discuss its usability. Rigorous security proof and security properties analysis show that our proposal is secure and reliable. Finally, through detailed comparisons on theoretical and experimental, both our two schemes show their superiority over the latest related works in terms of functionality and performance. The simulation evaluates and demonstrates the practicality of our solutions in practical IoT scenarios.
Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting

Photovoltaic (PV) power is highly stochastic and volatile, and PV power forecasting is a key technology to guarantee the safe and economic operation of high-penetration renewable power systems. To improve the accuracy of PV power forecasting, a quad-kernel deep convolutional neural network (QK_CNN) model is proposed to perform intra-hour PV power forecasting for the next four timesteps: four CNNs with different kernel sizes are used to extract different local cross features between sequence elements of four timesteps; a single-kernel CNN is used to further feature extraction of these features, and then the target sequence forecasting results are obtained; global maximum pooling method is used to simplify the feature extraction process and improve model learning efficiency. Operation data from a 26.52 kW PV plant in Central Australia is selected as the experimental data. Compared with single-kernel CNN and hybrid models (CNN_LSTM) on 5, 10, and 15 min of resolution data, respectively, the proposed model shows better forecasting performance and is able to explain 96 % to 98 % of the total variation of the forecasted PV power. All these demonstrates that the CNNs with specific design have great potential to handle the task of PV power forecasting as well.
Collective Writing: The Continuous Struggle for Meaning-Making

Abstract This paper is a summary of philosophy, theory, and practice arising from collective writing experiments conducted between 2016 and 2022 in the community associated with the Editors’ Collective and more than 20 scholarly journals. The main body of the paper summarises the community’s insights into the many faces of collective writing. Appendix 1 presents the workflow of the article’s development. Appendix 2 lists approximately 100 collectively written scholarly articles published between 2016 and 2022. Collective writing is a continuous struggle for meaning-making, and our research insights merely represent one milestone in this struggle. Collective writing can be designed in many different ways, and our workflow merely shows one possible design that we found useful. There are many more collectively written scholarly articles than we could gather, and our reading list merely offers sources that the co-authors could think of. While our research insights and our attempts at synthesis are inevitably incomplete, ‘Collective Writing: The Continuous Struggle for Meaning-Making’ is a tiny theoretical steppingstone and a useful overview of sources for those interested in theory and practice of collective writing.
Quantum algorithms for anomaly detection using amplitude estimation

Anomaly detection plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. Anomaly detection algorithm based on density estimation (called ADDE algorithm) is one of widely used algorithms. Liang et al. proposed a quantum version of the ADDE algorithm [Phys. Rev. A 99, 052310 (2019)] and it is believed that the algorithm has exponential speedups on both the number and the dimension of training data point over the classical algorithm. In this paper, we find that Liang et al.'s algorithm doesn't actually execute. Then we propose a new quantum ADDE algorithm based on amplitude estimation. It is shown that our algorithm can achieves exponential speedup on the number $M$ of training data points compared with the classical counterpart. Besides, the idea of our algorithm can be applied to optimize the anomaly detection algorithm based on kernel principal component analysis (called ADKPCA algorithm). Different from the quantum ADKPCA proposed by Liu et al. [Phys. Rev. A 97, 042315 (2018)], compared with the classical counterpart, which offer exponential speedup on the dimension $d$ of data points, our algorithm achieves exponential speedup on $M$.
Photosynthetically active radiation decomposition models for agrivoltaic systems applications

Decomposition models of solar irradiance estimate the magnitude of diffuse horizontal irradiance from global horizontal irradiance. These two radiation components are well known to be essential for predicting the performance of solar photovoltaic systems. In open-field agrivoltaic systems (i.e., the dual use of land for both agricultural activities and solar power conversion), cultivated crops receive unequal amounts of direct, diffuse, and reflected photosynthetically active radiation (PAR). These uneven amounts depend on where the crops are growing due to the non-homogenous shadings caused by the presence of the installed solar panels (above the crops or vertically mounted). It is known that, per unit of total PAR, diffuse PAR is more efficient for canopy photosynthesis than is direct PAR. For this reason, it is essential to estimate the diffuse PAR component when agrivoltaic systems are being assessed, in order to properly predict the crop yield. Since PAR is the electromagnetic radiation in the 400–700 nm waveband that can be used for photosynthesis by the crops, several stand-alone decomposition models typically used to split global horizontal irradiance are selected in this study to decompose PAR into direct and diffuse. These models are applied and validated in three locations in Sweden (Lanna, Hyltemossa and Norunda) using the coefficients stated on the models’ original publications and locally fitted coefficients. The results showed weaker performances in all stand-alone models for non-locally fitted coefficients (nRMSE ranging from 27% to 43%). However, performances improve with re-parameterization, with a highest nRMSE of 35.24% in Lanna. The Yang2 decomposition model is the best-performing one, with the lowest nRMSE of 23.75% in Norunda when applying re-estimated coefficients. Country level sets of coefficients for the best-performing models (Yang2 and Starke) are given after parameterization using combined data for all three locations in Sweden. These Sweden-fitted models are tested and show an nRMSE of 25.08% (Yang2) and 28.60% (Starke). These results can be used to perform estimations of the PAR diffuse component in Sweden wherever ground measurements are not available. The overall methodology can be similarly applied to other countries.
Governing cybersecurity from the boardroom: Challenges, drivers, and ways ahead

• Calls for increasing engagement by Boards of Directors with cybersecurity are increasing, but investigations of current practices are scarce. • We unpack the dynamics that characterize how directors engage with cybersecurity by analysing their drivers, to then suggest ways to increase such engagement. • Regulations have to be clarified, directors need to be trained in cybersecurity, and reporting practices have to be made clearer. Overall, the responsibility to oversee cyber-risk management in modern organisations lies with Boards of Directors. However, evidence suggests that boards are not nearly as engaged in cybersecurity as they are in other areas of oversight. Through the lens of neo-institutional theory, we investigated key drivers and major impediments to directors’ engagement with cybersecurity. We conducted 18 interviews with non-executive directors from 43 organisations to cast light on current cybersecurity practices and on the factors that drive directors’ engagement. Our findings emphasise that regulations are the most influential driver ( coercive pressures ). However, directors are not always completely aware of their duties and liability concerning cybersecurity oversight. Further, our study highlights that personal experience and background shape a director's engagement with cybersecurity ( normative forces ). Our analysis also shows a frequent over-reliance on a single board member with cyber-experience. Lastly, the secrecy that characterises cybersecurity reduces the opportunity for directors to replicate best practices across organisations ( mimetic forces ). Directors’ engagement with cybersecurity is marginally driven by holding multiple board roles and by the influence of external consultants. A stronger role is played by the mediatic nature of some cyber-breaches and by a prominent “push reporting” approach in cybersecurity ( organisational factors ). We offer a series of evidence-based practical recommendations to enhance directors’ engagement in this crucial area, ranging from strengthening existing regulations, to codifying best practices in cyber-reporting.
Feature selection via Non-convex constraint and latent representation learning with Laplacian embedding

• Calculate the similarity between pseudo-labels to keep complete sample information. • The latent representation space is learned in latent feature space and data space. • The latent representation contains the correlation of pseudo-labels. • Latent feature space provides discriminative information to guide feature selection. • Impose non-negative and l 2,1-2 -norm non-convex constraint on Q . In unsupervised feature selection, the relationship between pseudo-labels is often ignored, and the interconnection information between the data is not fully utilized. In order to solve these problems, this paper proposes a feature selection method via non-convex constraint and latent representation learning with Laplacian embedding (NLRL-LE). NLRL-LE keeps the correlation between the pseudo-labels to make the pseudo-label closer to the true label. And it combines with the interconnection information between data, learns the latent representation matrix to guide feature selection. Specifically, first, NLRL-LE regards each pseudo-label as a latent feature of the sample, constructs a latent feature graph, and retains the inherent attributes of the pseudo-labels. Second, latent representation learning is performed in the space which is made up of the latent feature space and data space. Since the latent feature graph retains the correlation between pseudo-labels, latent representation learning considers the interconnection information between data, and the information contained in the latent representation space is more complete. In addition, in order to make full use of pseudo-labels, the learned latent representation matrix is used as pseudo-label information to provide cluster labels in the latent representation space to guide feature selection. Finally, non-negative and l 2,1-2 -norm non-convex constraint are applied to the feature transformation matrix. The combination of non-negative constraint and non-convex constraint, compared with convex constraint , can ensure the row sparsity of the feature transformation matrix, select low-redundant features, and improve the feature selection effect. The experimental results show that the ACC and NMI of the NLRL-LE are better than the other seven compared algorithms on twelve datasets.
Do deep neural networks contribute to multivariate time series anomaly detection?

Anomaly detection in time series is a complex task that has been widely studied. In recent years, the ability of unsupervised anomaly detection algorithms has received much attention. This trend has led researchers to compare only learning-based methods in their articles, abandoning some more conventional approaches. As a result, the community in this field has been encouraged to propose increasingly complex learning-based models mainly based on deep neural networks. To our knowledge, there are no comparative studies between conventional, machine learning-based and, deep neural network methods for the detection of anomalies in multivariate time series. In this work, we study the anomaly detection performance of sixteen conventional, machine learning-based and, deep neural network approaches on five real-world open datasets. By analyzing and comparing the performance of each of the sixteen methods, we show that no family of methods outperforms the others. Therefore, we encourage the community to reincorporate the three categories of methods in the anomaly detection in multivariate time series benchmarks.
SoK: How Robust is Image Classification Deep Neural Network Watermarking?

Deep Neural Network (DNN) watermarking is a method for provenance verification of DNN models. Watermarking should be robust against watermark removal attacks that derive a surrogate model that evades provenance verification. Many watermarking schemes that claim robustness have been proposed, but their robustness is only validated in isolation against a relatively small set of attacks. There is no systematic, empirical evaluation of these claims against a common, comprehensive set of removal attacks. This uncertainty about a watermarking scheme’s robustness causes difficulty to trust their deployment in practice. In this paper, we evaluate whether recently proposed watermarking schemes that claim robustness are robust against a large set of removal attacks. We survey methods from the literature that (i) are known removal attacks, (ii) derive surrogate models but have not been evaluated as removal attacks, and (iii) novel removal attacks. Weight shifting and smooth retraining are novel removal attacks adapted to the DNN watermarking schemes surveyed in this paper. We propose taxonomies for watermarking schemes and removal attacks. Our empirical evaluation includes an ablation study over sets of parameters for each attack and watermarking scheme on the image classification datasets CIFAR-10 and ImageNet. Surprisingly, our study shows that none of the surveyed watermarking schemes is robust in practice. We find that schemes fail to withstand adaptive attacks and known methods for deriving surrogate models that have not been evaluated as removal attacks. This points to intrinsic flaws in how robustness is currently evaluated. Our evaluation includes a discussion of the runtime of each attack to underpin their practical relevance. While none of the schemes is robust against all attacks, none of the attacks removes all watermarks. We show that attacks can be combined and find combined attacks that remove all watermarks. We show that watermarking schemes need to be evaluated against a more extensive set of removal attacks with a more realistic adversary model. Our source code and a complete dataset of evaluation results are publicly available, which allows to independently verify our conclusions.
Sancus

Graph neural networks (GNNs) have emerged due to their success at modeling graph data. Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs come into play. To avoid communication caused by expensive data movement between workers, we propose Sancus, a staleness-aware communication-avoiding decentralized GNN system. By introducing a set of novel bounded embedding staleness metrics and adaptively skipping broadcasts, Sancus abstracts decentralized GNN processing as sequential matrix multiplication and uses historical embeddings via cache. Theoretically, we show bounded approximation errors of embeddings and gradients with convergence guarantee. Empirically, we evaluate Sancus with common GNN models via different system setups on large-scale benchmark datasets. Compared to SOTA works, Sancus can avoid up to 74% communication with at least 1.86X faster throughput on average without accuracy loss.
An Effective Cybersecurity Training Model to Support an Organizational Awareness Program

Traditional cybersecurity, security or information security awareness programs have become ineffective to change people's behavior in recognizing, failing to block or reporting cyberthreats within their organizational environment. As a result, human errors and actions continue to demonstrate that we are the weakest links in cybersecurity. This article studies the most recent cybersecurity awareness programs and its attributes. Furthermore, the authors compiled recent awareness methodologies, frameworks and approaches. The authors introduce a suggested awareness training model to address existing deficiencies in awareness training. The Cybersecurity Awareness TRAining Model (CATRAM) has been designed to deliver training to different organizational audiences, each of these groups with specific content and separate objectives. The authors concluded their study by addressing the need of future research to target new approaches to keep cybersecurity awareness focused on the everchanging cyberthreat landscape.
The Rise of Blockchain Internet of Things (BIoT): Secured, Device-to-Device Architecture and Simulation Scenarios

Most Internet of Things (IoT) resources are exposed to security risks due to their essential functionality. IoT devices, such as smartphones and tablets, have a limited network, computation, and storage capacity, making them more vulnerable to attacks. In addition, the huge volume of data generated by IoT devices remains an open challenge for existing platforms to process, analyze, and discover underlying trends to create a convenient environment. As a result, to deliver acceptable services, a new solution is necessary to secure data accountability, increase data privacy and accessibility, and extract hidden patterns and usable knowledge. Moving the Internet of Things to a distributed ledger system might be the most effective way to solve these issues. One of the most well-known and extensively utilized distributed ledger systems is the blockchain. Due to its unique properties, such as privacy, accountability, immutability, and anonymity, blockchain technology has recently attracted a lot of interest. Using IoT in conjunction with blockchain technology can bring several benefits. This paper reviews the current state of the art different BIoT architectures, with a focus on current technologies, applications, challenges, and opportunities. The test findings prove that the decentralized authentication platform-based blockchain-based IoT (BIoT) device-to-device architecture has a significantly higher throughput than the gateway-based architecture. To encrypt the elliptical curve cryptographic (ECC) and to generate keys, the Chinese remainder theorem (CRT)-based scheme is proposed and compared with the secure hash algorithm (SHA-256). Finally, ECC-CRT is used to access system performance in terms of latency, throughput, and resource consumption, simulated through the Contiki Cooja (CC) simulator, and alter orderer and peer nodes for performance study in BIoT. A comprehensive analysis and simulation results show that the proposed scheme is secure against a variety of known attacks, including the man-in-the-middle (MiM) attack, and outperforms the SHA-256 cryptographic algorithm. Moreover, the significance of blockchain and IoT, as well as their analysis of proposed architecture, is discussed. This paper will help readers and researchers understand the IoT and its applicability to the real world.
Stochastic stability analysis of particle swarm optimization with pseudo random number assignment strategy

Particle swarm optimization (PSO) is a population-based optimization method and has been successfully applied to solve many real-world problems. This method belongs to the stochastic optimization method and is mainly driven by two random streams utilized in the stochastic search mechanism, namely, individual (cognition) and social randomness effects. To our best knowledge, no research work has been conducted about the manipulation of the random stream assignment for stochastic search mechanism in the PSO algorithm. In this work, the influences of controlling randomness in the searching scheme of PSO is studied by introducing different pseudo random number (PRN) assignment strategies. The order-1 and order-2 stability analyses for particle dynamics under different PRN assignment strategies are also conducted to understand the influences. Stability analysis is carried out using the stochastic process theory. Our results show that the correlation caused by PRN has no effect on the unbiasedness of the expectation of particle position, but it would reduce or increase the variance of particle dynamics. Second, the convergent conditions of the PSO system under different PRN assignment strategies and the corresponding parameter selection ranges are provided. Finally, an empirical analysis via experimental simulations evaluated by six common swarm diversity measures, eight benchmark test functions, and two parameter tuples is presented.
Safe: Synergic Data Filtering for Federated Learning in Cloud-Edge Computing

With the increasing data scale in the Industrial Internet of Things, edge computing coordinated with machine learning is regarded as an effective way to raise the novel latency-sensitive services. To ensure the data privacy for frequent service access, federated learning (FL), as a privacy-preserving distributed framework, is integrated into edge computing, enabling user data invisible to the training process. However, sophisticated network attacks threaten deep learning (DL) models by data poison and malicious reasoning, making the DL-based system untrustworthy. To this end, a synergic data filtering method, named Safe, is proposed to deal with the poisoning attacks. Specifically, considering that the distributed support vector machine is at risk of being attacked due to its distribution and openness to communication, edge-cloud empowered FL framework is designed. Then, the alternating direction method of multipliers is deployed to detect attacked devices whose training processes will be interrupted. Moreover, due to the untrustworthiness of label data, the poisoned data in the attacked devices are figured out and filtered by clustering the trusted data with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means clustering algorithm. Eventually, extensive experiment results proved that the Safe outperforms correlation methods in detection accuracy and trustworthiness.