Reviewerly/exHarmony huggingface dataset:
sFarm: A Distributed Ledger Based Remote Crop Monitoring System for Smart Farming

Crop monitoring systems are one of the important aspects of Smart Agriculture. Due to explosive growth of population there is an increase in demand for food products while urbanization is causing shortage in manual labor. As the yield of a crop is greatly affected by many climatic and environmental parameters, there is an urgent need for efficient crop monitoring. Rapidly advancing IoT (Internet of Things) technologies have shown very promising results and have automated most of the traditional processes in farming. An efficient Crop Monitoring System (CMS) is proposed which automates the monitoring by using the IoT and real-time data is shared securely using private IOTA Tangle Distributed Ledger Technology. The proposed application equips farmers with required information which will help them make decisions promptly based on the real-time environmental parameters of the crop and reduces human labor. Data privacy and security are other important aspects addressed in the proposed system by setting up a private IOTA Tangle. Unlike public distributed ledgers, private distributed ledgers provide data privacy and security by allowing only known participants to join the network, thereby limiting the adversaries trying to tamper with the data. Practical implementation of the proposed system is done and analyzed for scalability and reliability.
Prediction on recommender system based on bi-clustering and moth flame optimization

Concerning the problems of weak scalability of traditional collaborative filtering recommender systems, a scalable recommender system based on bi-clustering and moth flame optimization algorithm is proposed. First of all, the users–items scoring matrix is filtered and cleaned in order to reduce the computational overhead, afterwards the bi-clustering data structures are constructed for the processed matrix, and the algorithm searches for bi-cluster containing the target user. Then, the results of bi-clustering are set as the initial population, and the moth flame optimization algorithm is applied to deeply optimize the similar users. Finally, the unrated items are predicted for the target user, and the recommendation list is generated for the target user. Validation experiments are carried on different scales of datasets; the results show that the proposed system achieves a good scalability, and also good recommendation performance.
<i>O</i>³HSC: Outsourced Online/Offline Hybrid Signcryption for Wireless Body Area Networks

Wireless body area networks (WBAN) enable ubiquitous monitoring of patients, which can change the future of healthcare services overwhelmingly. As the collected data of patients usually contain sensitive information, how to collect, transfer, store and share data securely and properly has become a concerning issue. Attribute-based encryption (ABE) can achieve data confidentiality and fine-grained access control simultaneously. Identity-based ring signature (IBRS) allows patients to prove their identity without leaking any extra (private) information. However, the heavy computational burden of ABE and IBRS is intolerable for most power-limited mobile devices, which account for a large proportion of WBAN devices. This paper combines the attribute-based online/offline encryption (ABOOE) and IBRS to achieve an outsourced online/offline hybrid signcryption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC) scheme. As far as we know, this scheme is the first signcryption scheme that adopts IBRS and satisfies online/offline signcryption simultaneously. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}$ </tex-math></inline-formula> HSC divides the key generation and signcryption into offline and online phases to increase the throughput of the central authority and save the power resources of mobile devices, respectively. Besides, outsourced decryption and public signature verification are also realized. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> achieves security under CCA and CMIA, and the performance analysis shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O^{3}\mathrm {HSC}$ </tex-math></inline-formula> is a lightweight and applicable scheme for WBAN.
Multibranch Adaptive Fusion Network for RGBT Tracking

RGBT tracking has been increasingly investigated in visual tracking due to the strong complementary nature of visible and infrared images. However, in the established RGBT tracking algorithms, multiscale information has not been well exploited and utilized, which limits the performance of the tracker. In this paper, a novel multibranch adaptive fusion network is proposed, which aggregates multiscale information from multiple branches. Specifically, our backbone network draws on the modified VGG-M. To extract the multiscale features, we design a multiscale adapter, which adds two small convolution kernel branches to the backbone in each layer and each modality in a parallel manner. We also design a multibranch fusion module to adaptively aggregate the features from multiple branches and the previous layer. Moreover, we propose a multimodal fusion module for aggregating features between modalities, which could mitigate the impact of noise from low-quality sources. Finally, many results on two recent RGBT tracking datasets show that our method significantly outperforms other state-of-the-art tracking methods.
Data driven approach to forecast the next day aggregate production of scattered small rooftop solar photovoltaic systems without meteorological parameters

Photovoltaic (PV) power has became an attractive research subject, despite the variability and uncertainty of the phenomenon, PV power forecasting is considered as a promising solution for successful PV power integration, and also an important decision making information for both, grid operators and energy traders. Forecasting the aggregated PV power in different spatiotemporal scales is very relevant for grid operators, but unfortunately, in the state of the art, few works deal with this subject. In the aim to forecast directly the next day aggregate photovoltaic power of scattered small rooftop installations based only on historical production data, we propose in this paper a hybrid nonlinear autoregressive regression model, composed from a combination of empirical mode decomposition (EMD), stepwise regression (SWR) and least square support vector regression (LsSVR) techniques. To address the lack of meteorological parameters, we propose a different data preprocessing strategy, this strategy will also allow capturing the diurnal component of the PV power time series in an efficient way. An in depth analysis of the proposed approach using publicly available database was conducted. We compared the results of the proposed approach with three other models, two nonlinear autoregressive models based on LsSVR (AR-LsSVR) and FFNN (AR-FFNN), and the third one is a persistent forecaster. Three accuracy metrics were adopted to compare the different models, the nMAE, nMBE and nMRSE. In general good results were obtained in clear and partially cloudy days, but there are few days characterized by sudden change in the time series pattern that the model cant handle. But still competitive results are obtained, the hybrid model gives an nMAE = 4.12% compared to 4.94%, 4.95% and 164.98% for AR-LsSVR, AR-FFNN and the persistent forecaster respectively. The projection of the obtained results on five recently developed models shows the effectiveness of the proposed approach.
Few-shot image classification with composite rotation based self-supervised auxiliary task

Many real-life problem settings have classes of data with very few examples for training. Deep learning networks do not perform well for such few-shot classes. In order to perform well in this setting, the networks should learn to extract highly discriminative and generic features. In this paper, we propose to use a composite rotation based self-supervised auxiliary task to improve the representation learning of the network so that it can extract such discriminative features. Our proposed composite rotation based auxiliary task rotates the image at two levels, i.e., it rotates patches inside the image (inner rotation) and also rotates the whole image (outer rotation), and assigns one out of 16 rotation classes to the transformed image. We jointly train the network on the main image classification task and the composite rotation based auxiliary task. This helps the network to learn to extract more generic and discriminative features, which in turn helps to improve its few-shot classification performance. Additionally, during the few-shot testing phase, we consolidate the predictions for images obtained by applying different composite rotation transformations to the same query image to improve the final prediction further. We perform experiments on several few-shot benchmark datasets and empirically show the efficacy of our method for various problem settings. We experimentally show that our method improves the performance of deep learning models on the few-shot classification, fine-grained few-shot classification, cross-domain few-shot classification, and transductive few-shot classification settings. We also experimentally show that models trained using our approach perform better than the baseline even when the query examples in the episode are not aligned with the support examples in the episode. We perform extensive ablation experiments to validate the different components of our approach. We also analyze the effect of our approach on the ability of the network to focus on the discriminative regions of the image.
Research on Medical Waste Supervision Model and Implementation Method Based on Blockchain

The Internet of Things (IoT) has brought unprecedented changes to the society and permeated our daily life. However, it has not been successfully applied in the area of medical waste regulation, where the recycling and disposal of medical waste have significant loopholes in the management of classification, transportation, disposal, supervision, and other links. The source, authenticity, and integrity of medical waste data lack guarantees, and there is a risk of data tampering and forgery. Although there are currently some medical waste supervision applications combined with IoT-based blockchain domestically and internationally to facilitate information sharing and transfer, no verifiable method is provided for the information privacy leakage of medical waste operators. To address this problem, we propose a blockchain-based medical waste supervision model, which connects participants involved in the process, introduces digital credentials to achieve the protection of operator information privacy, and ensures that the entire data process is authentic and credible. By building a decentralized system architecture and setting intelligent contracts, we integrate and record the medical waste disposal regulatory information in different phases on the blockchain to form the supervision of medical waste chain. In addition, we digitize the physical credentials and certificates using digital credentials to achieve cryptography security and privacy protection. The regulatory model designed in this paper can provide digital certificates of disposal tracking information to the health, environmental protection, and other administrative departments in China. It can provide authoritative evidence for the supervision and accountability of medical waste disposal and support the construction of a new generation of medical waste regulatory information systems in China.
Cloud Data Encryption and Authentication Based on Enhanced Merkle Hash Tree Method

Many organizations apply cloud computing to store and effectively process data for various applications. The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity. In this research, an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data. Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data. Merkle Hash tree provides the efficient mapping of data and easily identifies the changes made in the data due to proper structure. The developed model supports privacy-preserving public auditing to provide a secure cloud storage system. The data owners upload the data in the cloud and edit the data using the private key. An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches. The data files requested by the data owner are audit by a third-party auditor and the multi-owner authentication method is applied during the modification process to authenticate the user. The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.
A Large-Scale Study on the Security Vulnerabilities of Cloud Deployments

As cloud deployments are becoming ubiquitous, the rapid adoption of this new paradigm may potentially bring additional cyber security issues. It is crucial that practitioners and researchers pose questions about the current state of cloud deployment security. By better understanding existing vulnerabilities, progress towards a more secure cloud can be accelerated. This is of paramount importance especially with more and more critical infrastructures moving to the cloud, where the consequences of a security incident can be significantly broader. This study presents a data-centric approach to security research – by using three static code analysis tools and scraping the internet for publicly available codebases, a footprint of the current state of open-source infrastructure-as-code repositories can be achieved. Out of the scraped 44485 repository links, the study is concentrated on 8256 repositories from the same cloud provider, across which 292538 security violations have been collected. Our contributions consist of: understanding on existing security vulnerabilities of cloud deployments, contributing a list of Top Guidelines for practitioners to follow to securely deploy systems in the cloud, and providing the raw data for further studies.
Aligned metric representation based balanced multiset ensemble learning for heterogeneous defect prediction

Heterogeneous defect prediction (HDP) refers to the defect prediction across projects with different metrics. Most existing HDP methods map source and target data into a common metric space where each dimension has no actual meaning, which weakens their interpretability. Besides, HDP always suffers from the class imbalance problem. For deficiencies of current HDP methods, we intend to propose a novel HDP approach that can reduce the heterogeneity of source and target data and deal with imbalanced data while retaining the actual meaning for each dimension of constructed common metric space. We propose an Aligned Metric Representation based Balanced Multiset Ensemble learning (BMEL+ AMR) approach for HDP. AMR consists of shared, source-specific, and target-specific metrics. It is built by learning the translation from shared to specific metrics and reducing the distribution difference. To deal with imbalanced data, we design BMEL that constructs multiple balanced subsets for source data and produces an aggregated classifier for predicting labels of target data. Experimental results on 22 public projects indicate that (1) among all competing methods, BMEL+AMR achieves the best performance on all indicators except Popt, followed by AMR; (2) compared with AMR, the introduction of BMEL improves the performance on non-effort-aware indicators statistically significantly except F1-score; compared with BMEL, the introduction of AMR improves the performance throughout all indicators statistically significantly. BMEL+AMR can effectively improve HDP performance by eliminating heterogeneity and dealing with imbalanced data, and AMR is helpful to explain the prediction model.
Multi-Modal Multi-Action Video Recognition

Multi-action video recognition is much more challenging due to the requirement to recognize multiple actions co-occurring simultaneously or sequentially. Modeling multi-action relations is beneficial and crucial to understand videos with multiple actions, and actions in a video are usually presented in multiple modalities. In this paper, we propose a novel multi-action relation model for videos, by leveraging both relational graph convolutional networks (GCNs) and video multi-modality. We first build multi-modal GCNs to explore modality-aware multi-action relations, fed by modality-specific action representation as node features, i.e., spatiotemporal features learned by 3D convolutional neural network (CNN), audio and textual embeddings queried from respective feature lexicons. We then joint both multi-modal CNN-GCN models and multi-modal feature representations for learning better relational action predictions. Ablation study, multi-action relation visualization, and boosts analysis, all show efficacy of our multi-modal multi-action relation modeling. Also our method achieves state-of-the-art performance on large-scale multi-action M-MiT benchmark. Our code is made publicly available at https://github.com/zhenglab/multi-action-video.
Statistical analysis of power generation of semi-transparent photovoltaic (STPV) for diversity in building envelope design: A mock-up test by azimuth and tilt angles

Semi-transparent photovoltaics (STPVs) have received increasing attention as an energy-efficient building envelope that uses renewable energy. It is necessary to find the optimal combination of the azimuth angle and tilt angle to install the STPV and to secure high-power generation. However, various constraints limit the installation of STPVs at optimal angles. In other words, there may be a conflict between the building envelope design and STPV design depending on the circumstances, thereby limiting the design. Therefore, this study presents a statistical analysis of power generation with respect to angle to determine the possibilities in building envelope design. Two identical STPV modules each were installed at five azimuth and tilt angles (i.e., two STPV modules each on 25 sides), and the power generation for one year was measured. Then, 1392 h of data were sampled and a statistical analysis was conducted to compare the power generation. First, the representative values were set after comparing the power generation of the two modules at each angle through the Mann–Whitney test. Then, the power generation of each angle was compared through the Kruskal–Wallis test, and a post hoc analysis was conducted. As a result, power generation for five azimuth angles (e.g., +90° (west), +45° (southwest), 0° (south), −45° (southeast), and −90° (east)) was statistically identical in performance at tilt angles of 3° and 15°. Therefore, it is more desirable to focus designs on securing more area than considering optimal azimuth angle in the generation of maximum power through STPVs, at tilt angles of 3° and 15°. Moreover, STPVs for the tilt angles of 75° and 90° were statistically identical in performance when facing west, southwest, southeast, and east. This suggests that, when an STPV is installed at 75° or 90° of the tilt angle, if it cannot be installed on the south, it will provide statistically identical performance even if it is installed at any other azimuth angle. In light of these findings, this study concludes by providing guidelines for the application of STPVs in early architectural design.
A Bayesian Framework for the Analysis and Optimal Mitigation of Cyber Threats to Cyber‐Physical Systems

Critical infrastructures are increasingly reliant on information and communications technology (ICT) for more efficient operations, which, at the same time, exposes them to cyber threats. As the frequency and severity of cyberattacks are increasing, so are the costs of critical infrastructure security. Efficient allocation of resources is thus a crucial issue for cybersecurity. A common practice in managing cyber threats is to conduct a qualitative analysis of individual attack scenarios through risk matrices, prioritizing the scenarios according to their perceived urgency and addressing them in order until all the resources available for cybersecurity are spent. Apart from methodological caveats, this approach may lead to suboptimal resource allocations, given that potential synergies between different attack scenarios and among available security measures are not taken into consideration. To overcome this shortcoming, we propose a quantitative framework that features: (1) a more holistic picture of the cybersecurity landscape, represented as a Bayesian network (BN) that encompasses multiple attack scenarios and thus allows for a better appreciation of vulnerabilities; and (2) a multiobjective optimization model built on top of the said BN that explicitly represents multiple dimensions of the potential impacts of successful cyberattacks. Our framework adopts a broader perspective than the standard cost-benefit analysis and allows the formulation of more nuanced security objectives. We also propose a computationally efficient algorithm that identifies the set of Pareto-optimal portfolios of security measures that simultaneously minimize various types of expected cyberattack impacts, while satisfying budgetary and other constraints. We illustrate our framework with a case study of electric power grids.
Sign steganography revisited with robust domain selection

To enable covert communication in the real world, robust steganography has attracted widespread attention. In this paper, a novel DCT sign-based robust steganography with robust domain selection is proposed, named Sign Steganography Revisited (SSR). The prior arts ensure the robustness by selecting robust cover elements, while the data embedding might make the selected elements invalid. This inspired SSR to select invariant stego elements before and after compression; meanwhile, error correction coding is removed to improve undetectability. Specifically, all the embedding domains are represented by the 8×8 DCT block, and the robust embedding domains with invariant stego elements are further selected. Therefore, the shared location information of selected domains is remarkably reduced. Besides, the error-free extraction is proved theoretically under compression within a specific range. Moreover, the undetectability of SSR is improved by correcting the distortion function. Experimental results show the effectiveness of the proposed SSR in lossy channels; the practicability of SSR is also verified on social network platforms.