A super SDM (species distribution model) 'in the cloud' for better habitat-association inference with a 'big data' application of the Great Gray Owl for Alaska.

The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access 'Big Data' sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest-an ensemble-type model of Machine Learning following Leo Breiman's approach of inference from predictions-we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors ('Big Data'). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a 'new' baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka 'Phantom of the North'). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.

With super SDMs (machine learning, open access big data, and the cloud) towards more holistic global squirrel hotspots and coldspots.

Species-habitat associations are correlative, can be quantified, and used for powerful inference. Nowadays, Species Distribution Models (SDMs) play a big role, e.g. using Machine Learning and AI algorithms, but their best-available technical opportunities remain still not used for their potential e.g. in the policy sector. Here we present Super SDMs that invoke ML, OA Big Data, and the Cloud with a workflow for the best-possible inference for the 300 + global squirrel species. Such global Big Data models are especially important for the many marginalized squirrel species and the high number of endangered and data-deficient species in the world, specifically in tropical regions. While our work shows common issues with SDMs and the maxent algorithm ('Shallow Learning'), here we present a multi-species Big Data SDM template for subsequent ensemble models and generic progress to tackle global species hotspot and coldspot assessments for a more inclusive and holistic inference.

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

Quantile regression shrinkage and selection via the Lqsso.

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

Toward Sensor Measurement Reliability in Blockchains.

In this work, a secure architecture to send data from an Internet of Things (IoT) device to a blockchain-based supply chain is presented. As is well known, blockchains can process critical information with high security, but the authenticity and accuracy of the stored and processed information depend primarily on the reliability of the information sources. When this information requires acquisition from uncontrolled environments, as is the normal situation in the real world, it may be, intentionally or unintentionally, erroneous. The entities that provide this external information, called Oracles, are critical to guarantee the quality and veracity of the information generated by them, thus affecting the subsequent blockchain-based applications. In the case of IoT devices, there are no effective single solutions in the literature for achieving a secure implementation of an Oracle that is capable of sending data generated by a sensor to a blockchain. In order to fill this gap, in this paper, we present a holistic solution that enables blockchains to verify a set of security requirements in order to accept information from an IoT Oracle. The proposed solution uses Hardware Security Modules (HSMs) to address the security requirements of integrity and device trustworthiness, as well as a novel Public Key Infrastructure (PKI) based on a blockchain for authenticity, traceability, and data freshness. The solution is then implemented on Ethereum and evaluated regarding the fulfillment of the security requirements and time response. The final design has some flexibility limitations that will be approached in future work.

Oracle selection provides insight into how far off practice is from Utopia in plant breeding.

Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution.

Blockchain-enhanced certificateless signature scheme in the standard model.

The Internet of Things (IoT), driven by wireless communication and other technologies, is gradually entering our lives and promoting the transformation of society from "informatization" to "intelligence". Certificateless signature (CLS) eliminates the characteristic of certificate management, making it an effective method for verifying large-scale data in the IoT environment. Nevertheless, hash functions are regarded as ideal random oracles in the security proofs of most CLS schemes, which cannot guarantee the security of CLS schemes in reality. In response to this problem, Shim devised a CLS scheme without random oracles in the standard model and declared it to be provably secure. Unfortunately, in this paper, we cryptanalyze Shim's CLS scheme and demonstrate that it is not resistant to public key replacement attacks from a Type Ⅰ attacker. Furthermore, to further improve the security of the Shim CLS scheme and avoid the single-point failure of the KGC and the signature forgery initiated, we propose a blockchain-based CLS scheme without a random oracle. Finally, we evaluate the comprehensive performance, and while maintaining the computational and communication performance of the Shim scheme, we resist both Type Ⅰ and Type Ⅱ attackers, as well as signature forgery initiated against public parameters.

Subgradient ellipsoid method for nonsmooth convex problems.

In this paper, we present a new ellipsoid-type algorithm for solving nonsmooth problems with convex structure. Examples of such problems include nonsmooth convex minimization problems, convex-concave saddle-point problems and variational inequalities with monotone operator. Our algorithm can be seen as a combination of the standard Subgradient and Ellipsoid methods. However, in contrast to the latter one, the proposed method has a reasonable convergence rate even when the dimensionality of the problem is sufficiently large. For generating accuracy certificates in our algorithm, we propose an efficient technique, which ameliorates the previously known recipes (Nemirovski in Math Oper Res 35(1):52-78, 2010).

CCIO: A Cross-Chain Interoperability Approach for Consortium Blockchains Based on Oracle.

Cross-chain interoperability can expand the ability of data interaction and value circulation between different blockchains, especially the value interaction and information sharing between industry consortium blockchains. However, some current public blockchain cross-chain technologies or data migration schemes between consortium blockchains need help to meet the consortium blockchain requirements for efficient two-way data interaction. The critical issue to solve in cross-chain technology is improving the efficiency of cross-chain exchange while ensuring the security of data transmission outside the consortium blockchain. In this article, we design a cross-chain architecture based on blockchain oracle technology. Then, we propose a bidirectional information cross-chain interaction approach (CCIO) based on the former architecture, we novelly improve three traditional blockchain oracle patterns, and we combine a mixture of symmetric and asymmetric keys to encrypt private information to ensure cross-chain data security. The experimental results demonstrate that the proposed CCIO approach can achieve efficient and secure two-way cross-chain data interactions and better meet the application needs of large-scale consortium blockchains.

Variable selection for bivariate interval-censored failure time data under linear transformation models.

Variable selection is needed and performed in almost every field and a large literature on it has been established, especially under the context of linear models or for complete data. Many authors have also investigated the variable selection problem for incomplete data such as right-censored failure time data. In this paper, we discuss variable selection when one faces bivariate interval-censored failure time data arising from a linear transformation model, for which it does not seem to exist an established procedure. For the problem, a penalized maximum likelihood approach is proposed and in particular, a novel Poisson-based EM algorithm is developed for the implementation. The oracle property of the proposed method is established, and the numerical studies suggest that the method works well for practical situations.

A general framework of nonparametric feature selection in high-dimensional data.

Nonparametric feature selection for high-dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may suffer from model misspecification. In this paper, we propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space. The space is generated by a novel tensor product kernel, which depends on a set of parameters that determines the importance of the features. Computationally, we minimize the empirical risk with a penalty to estimate the prediction and kernel parameters simultaneously. The solution can be obtained by iteratively solving convex optimization problems. We study the theoretical property of the kernel feature space and prove the oracle selection property and Fisher consistency of our proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via extensive simulation studies and applications to two real studies.

An Ansatz for Computational Undecidability in RNA Automata.

In this ansatz we consider theoretical constructions of RNA polymers into automata, a form of computational structure. The bases for transitions in our automata are plausible RNA enzymes that may perform ligation or cleavage. Limited to these operations, we construct RNA automata of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing machine equivalent 2-stack PDA (RNA-2PDA) and the universal RNA-UPDA. For each automaton we show how the enzymatic reactions match the logical operations of the RNA automaton. A critical theme of the ansatz is the self-reference in RNA automata configurations that exploits the program-data duality but results in computational undecidability. We describe how computational undecidability is exemplified in the self-referential Liar paradox that places a boundary on a logical system, and by construction, any RNA automata. We argue that an expansion of the evolutionary space for RNA-2PDA automata can be interpreted as a hierarchical resolution of computational undecidability by a meta-system (akin to Turing's oracle), in a continual process analogous to Turing's ordinal logics and Post's extensible recursively generated logics. On this basis, we put forward the hypothesis that the resolution of undecidable configurations in RNA automata represent a novelty generation mechanism and propose avenues for future investigation of biological automata.

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach.

BACKGROUND: Precision medicine is a promising approach that has revolutionized disease prevention and individualized treatment. The DELFOS oracle is a model-driven genomics platform that aids clinicians in identifying relevant variations that are associated with diseases. In its previous version, the DELFOS oracle did not consider the high degree of variability of genomics data over time. However, changes in genomics data have had a profound impact on clinicians' work and pose the need for changing past, present, and future clinical actions. Therefore, our objective in this work is to consider changes in genomics data over time in the DELFOS oracle. METHODS: Our objective has been achieved through three steps. First, we studied the characteristics of each database from which the DELFOS oracle extracts data. Second, we characterized which genomics concepts of the conceptual schema that supports the DELFOS oracle change over time. Third, we updated the DELFOS Oracle so that it can manage the temporal dimension. To validate our approach, we carried out a use case to illustrate how the new version of the DELFOS oracle handles the temporal dimension. RESULTS: Three events can change genomics data, namely, the addition of a new variation, the addition of a new link between a variation and a phenotype, and the update of a link between a variation and a phenotype. These events have been linked to the entities of the conceptual model that are affected by them. Finally, a new version of the DELFOS oracle that can deal with the temporal dimension has been implemented. CONCLUSION: Huge amounts of genomics data that is associated with diseases change over time, impacting patients' diagnosis and treatment. Including this information in the DELFOS oracle added an extra layer of complexity, but using a model-driven based approach mitigated the cost of implementing the needed changes. The new version handles the temporal dimension appropriately and eases clinicians' work.

A robust and efficient variable selection method for linear regression.

Variable selection is fundamental to high dimensional statistical modeling, and many approaches have been proposed. However, existing variable selection methods do not perform well in presence of outliers in response variable or/and covariates. In order to ensure a high probability of correct selection and efficient parameter estimation, we investigate a robust variable selection method based on a modified Huber's function with an exponential squared loss tail. We also prove that the proposed method has oracle properties. Furthermore, we carry out simulation studies to evaluate the performance of the proposed method for both pn. Our simulation results indicate that the proposed method is efficient and robust against outliers and heavy-tailed distributions. Finally, a real dataset from an air pollution mortality study is used to illustrate the proposed method.

Polar biodiversity data: From a national marine platform to a global data portal.

Global access to accurate biodiversity data is a prerequisite to our progress in understanding biodiversity dynamics in ecosystems and any changes that are occurring. Despite recent major advancements in sharing data on the world's species, one of the remaining challenges relates to the mechanics of guiding data systematically from its provenance to end users. It can take considerable effort to orchestrate a successful sampling campaign, manage samples obtained in often extreme, remote conditions and to secure preservation of, and access to, the acquired data. Here, we briefly describe biodiversity data flow from a polar ship to a national data repository and onward to a global data portal. This paper highlights a few crucial points in this process, which aims to provide information systematically into the mosaic of our polar species biodiversity knowledge. This flexible workflow can be modified for other data types and adopted by other data repositories.

Sarve: synthetic data and local differential privacy for private frequency estimation.

The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.

Structured sparse support vector machine with ordered features.

In the application of high-dimensional data classification, several attempts have been made to achieve variable selection by replacing the ℓ 2 -penalty with other penalties for the support vector machine (SVM). However, these high-dimensional SVM methods usually do not take into account the special structure among covariates (features). In this article, we consider a classification problem, where the covariates are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. We propose a structured sparse SVM to tackle this type of problems, which combines the non-convex penalty and cubic spline estimation procedure (i.e. penalizing second-order derivatives of the coefficients) to the SVM. From a theoretical point of view, the proposed method satisfies the local oracle property. Simulations show that the method works effectively both in feature selection and classification accuracy. A real application is conducted to illustrate the benefits of the method.

ATS-LIA: A lightweight mutual authentication based on adaptive trust strategy in flying ad-hoc networks.

With the rapid development of wireless communication and edge computing, UAV-assisted networking technology has great significance in many application scenarios such as traffic forecasting, emergency rescue, military reconnaissance. However, due to dynamic topology changes of Flying Ad-hoc Networks (FANET), frequent identity authentication is easy to cause the instability of communications between UAV nodes, which makes FANET face serious identity security threats. Therefore, it is an inevitable trend to build a secure and reliable FANET. In this paper, we propose a lightweight mutual identity authentication scheme based on adaptive trust strategy for Flying Ad-hoc Networks (ATS-LIA), which selects the UAV with the highest trust value from the UAV swarm to authenticate with the ground control station (GCS). While ensuring the communication security, we reduce the energy consumption of UAV to the greatest extent, and reduce the frequent identity authentication between UAV and GCS. Through the security game verification under the random oracle model, it is proved that the proposed method can effectively resist some attacks, effectively reduce the computational overhead, and ensure the communication security of FANET. The results show that compared with the existing schemes, the proposed ATS-LIA scheme has lower computational overhead.

RAFI: Robust Authentication Framework for IoT-Based RFID Infrastructure.

The Internet of Things (IoT) is a future trend that uses the Internet to connect a variety of physical things with the cyber world. IoT technology is rapidly evolving, and it will soon have a significant impact on our daily lives. While the growing number of linked IoT devices makes our daily lives easier, it also puts our personal data at risk. In IoT applications, Radio Frequency Identification (RFID) helps in the automatic identification of linked devices, and the dataflow of the system forms a symmetry in communication between the tags and the readers. However, the security and privacy of RFID-tag-connected devices are the key concerns. The communication link is thought to be wireless or insecure, making the RFID system open to several known threats. In order to address these security issues, we propose a robust authentication framework for IoT-based RFID infrastructure. We use formal security analysis in the random oracle model, as well as information analysis to support the claim of secure communication. Regarding the desirable performance characteristics, we describe and analyze the proposed framework's performance and compare it to similar systems. According to our findings, the proposed framework satisfies all security requirements while also improving the communication.

RAPCHI: Robust authentication protocol for IoMT-based cloud-healthcare infrastructure.

With the fast growth of technologies like cloud computing, big data, the Internet of Things, artificial intelligence, and cyber-physical systems, the demand for data security and privacy in communication networks is growing by the day. Patient and doctor connect securely through the Internet utilizing the Internet of medical devices in cloud-healthcare infrastructure (CHI). In addition, the doctor offers to patients online treatment. Unfortunately, hackers are gaining access to data at an alarming pace. In 2019, 41.4 million times, healthcare systems were compromised by attackers. In this context, we provide a secure and lightweight authentication scheme (RAPCHI) for CHI employing Internet of medical Things (IoMT) during pandemic based on cryptographic primitives. The suggested framework is more secure than existing frameworks and is resistant to a wide range of security threats. The paper also explains the random oracle model (ROM) and uses two alternative approaches to validate the formal security analysis of RAPCHI. Further, the paper shows that RAPCHI is safe against man-in-the-middle and reply attacks using the simulation programme AVISPA. In addition, the paper compares RAPCHI to related frameworks and discovers that it is relatively light in terms of computation and communication. These findings demonstrate that the proposed paradigm is suitable for use in real-world scenarios.