Reviewerly/exHarmony huggingface dataset:
Dynamic fault tolerant scheduling with response time minimization for multiple failures in cloud

With the increasing demand for large amount of computing resources, the cloud is widely used for executing large number of independent tasks. In order to successfully execute more tasks and maximize the revenues, the cloud service providers (CSPs) should provide reliable services, while maximizing the resource utilization. Providing better Quality of Service (QoS), while maximizing the resource utilization in the event of failures is a critical research issue which needs to be addressed. In this paper, an Elastic pull-based Dynamic Fault Tolerant (E-DFT) scheduling mechanism is designed for minimizing the response time while executing the backups during multiple failures of independent tasks. A basic core primary backup model is also used and integrated with the backup tasks overlapping (BTO) and backup tasks fusion (BTF) techniques to tolerate multiple simultaneous failures. Simulation results show that the proposed E-DFT scheduling can achieve better performance in terms of guarantee ratio and resource utilization over other existing scheduling algorithms.
Cervical optical coherence tomography image classification based on contrastive self‐supervised texture learning

Cervical cancer (CC) seriously affects the health of the female reproductive system. Optical coherence tomography (OCT) emerged as a noninvasive, high-resolution imaging technology for cervical disease detection. However, OCT image annotation is knowledge-intensive and time-consuming, which impedes the training process of deep-learning-based classification models.This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning.In addition to high-level semantic features extracted by a convolutional neural network (CNN), the proposed CADx approach designs a contrastive texture learning strategy to leverage unlabeled cervical OCT images' texture features. We conducted 10-fold cross-validation on the OCT image dataset from a multicenter clinical study on 733 patients from China.In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion and CC, our method achieved an area-under-the-curve value of 0.9798 ± 0.0157 with a sensitivity of 91.17% ± 4.99% and a specificity of 93.96% ± 4.72% for OCT image patches; also, it outperformed two out of four medical experts on the test set. Furthermore, our method achieved 91.53% sensitivity and 97.37% specificity on an external validation dataset containing 287 three-dimensional OCT volumes from 118 Chinese patients in a new hospital using a cross-shaped threshold voting strategy.The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat."
Hate speech Detection in Asian Languages:A Survey

In this study, we present a language-based survey of hate speech detection in Asian languages. The motivation of this survey is to encourage the development of an automated hate speech detection system for Malayalam. Any message from social media spreading negativity in the society related to sex, caste, religion, politics, race etc. can be called a hateful message. This kind of text is very challenging to detect. Here we have taken only language-specific studies for hate speech detection and analyzed the approaches used in each work. We have used three parameters in this paper to analyze the overall scenario of this problem among Asian languages. This study tries to identify the best classification algorithm for this task and also find the relation between classification approach, type and size of dataset and accuracy. So this survey will become the foundation of future studies in this area and will help to understand the challenges also.
Vent distribution and sub-volcanic systems: Myths, fallacies, and some plausible facts

Characterization of spatially distributed volcanism includes analysis of vent alignments, zones of high probability density and cluster membership assignment. Attempts to identify patterns from the spatial distribution of vents have resorted to various methods with the underlying assumption that results from any of those methods should provide quantitative evidence concerning the structure of the array. In contrast, the physical conditions leading to the creation of specific patterns of distribution have received less attention. In this work, the basis for some of the common expectations of studies of vent distribution, and how those expectations match the individual method-inherent assumptions, are reviewed. Throughout this work it is shown that the spatial distribution of volcanic vents allows us to extract much information concerning the physical structure of the sub-volcanic system. Nevertheless, attention must be given to the fact that not all systems are equal, and therefore, that it is a myth that any quantitative method can provide reliable information. Furthermore, it is shown that some methods even might yield false clues leading to the statistical validation of structures that have no physical support. Thus, in this paper it is highlighted that the selection of methods that fulfill the physical constraints likely to exist on the case of interest is extremely important to promote identification of the most plausible configurations in a robust form. Failure to acknowledge the importance of method selection can be detrimental for the correct understanding of the physical mechanisms underlying volcanic activity, and therefore such practices need to be avoided.
A neural database for differentially private spatial range queries

Mobile apps and location-based services generate large amounts of location data. Location density information from such datasets benefits research on traffic optimization, context-aware notifications and public health (e.g., disease spread). To preserve individual privacy, one must sanitize location data, which is commonly done using differential privacy (DP). Existing methods partition the data domain into bins, add noise to each bin and publish a noisy histogram of the data. However, such simplistic modelling choices fall short of accurately capturing the useful density information in spatial datasets and yield poor accuracy. We propose a machine-learning based approach for answering range count queries on location data with DP guarantees. We focus on countering the sources of error that plague existing approaches (i.e., noise and uniformity error) through learning, and we design a neural database system that models spatial data such that density features are preserved, even when DP-compliant noise is added. We also devise a framework for effective system parameter tuning on top of public data, which helps set important system parameters without expending scarce privacy budget. Extensive experimental results on real datasets with heterogeneous characteristics show that our proposed approach significantly outperforms the state of the art.
A Case for Pricing Bandwidth: Sharing Datacenter Networks With Cost Dominant Fairness

Unlike other resources such as CPU or memory in a virtual machine, inter-virtual-machine (inter-VM) bandwidth has not been explicitly priced in datacenter networks. In this article, we argue that tenants of an IaaS cloud computing platform should be given the flexibility to pay more for explicitly priced datacenter bandwidth beyond traditional virtual machines, in order to achieve better (or more predictable) application performance. We show that a much simpler design principle can be followed to allocate bandwidth fairly, and desirable properties related to fairness can be more easily achieved, compared with state-of-the-art proposals. We call such a design principle cost dominant fairness, which stipulates that bandwidth should be allocated based on the total cost that a tenant incurs for running its applications in the cloud. Guided by the principle of cost dominant fairness, we explore the design space of pricing inter-VM bandwidth, as well as achieving fair bandwidth sharing among multiple tenants. Through our study, we believe that it is best to assign per-VM-pair weights based on individualized prices. We present a distributed bandwidth allocation algorithm that is theoretically supported by a network utility maximization formulation, and practically implemented as a shim layer at each virtual machine. We are also concerned with practical issues of billing, where discounts are needed to ensure that a tenant only pays for the bandwidth share that it is allocated. Finally, we have evaluated our pricing framework and per-VM-pair weighted fair bandwidth allocation in the Mininet emulation testbed and simulations.
Scaling the Blockchain-based Access Control Framework for IoT via Sharding

Access control is one of the important means to protect privacy in the Internet of Things (IoT), and access control model is mostly based on central trusted entity. The decentralized blockchain technology makes up for the shortcomings of the existing access control model for IoT. However, the transaction generation rate in IoT has brought huge challenges to the scalability of the integration framework of blockchain and IoT, including the Transaction Processing Speed (TPS) and the storage of each node.This paper proposes the Network Sharding Scheme (NsScheme) to improve the scalability of the blockchain-based access control framework in IoT. Based on the three-tier architecture of "cloud-edge-device" in IoT, we divide edge nodes into several shards. Each shard maintains a local blockchain, and cloud nodes maintain a global blockchain. Multiple blockchains process transactions in parallel to improve the Transaction Processing Speed (TPS) and reduce the storage of each node. In the meantime, we present the transactions allocation scheme that which shard processing the transaction depends on both parties in transaction, which can reduce the routing cost of querying transactions to O(1). Considering that the number of cross-shard transactions will affect the query cost of cross-shard transactions, we further introduce the network sharding algorithm based on Access Frequency Set (AFS) between nodes, which can effectively reduce the query cost of cross-shard transactions. The simulation results indicate that with the increase of the number of shards, NsScheme can linearly increase the TPS and reduce the storage of each node.
Fast Evolutionary Neural Architecture Search Based on Bayesian Surrogate Model

Neural Architecture Search (NAS) is studied to automatically design the deep neural network structure, freeing people from heavy network design tasks. Traditional NAS based on individual performance evaluation needs to train many networks generated by the search, and compare the performance of the networks according to their accuracy, which is very time-consuming. In this study, we propose to use a two-category comparator based random forest model as a surrogate to estimate the accuracy of the networks. thereby reducing heavy network training process and greatly saving search time. Instead of directly predicting the accuracy of each network, we propose to compare the relative performance between each two networks in our proposed two-category comparator. Furthermore, we implement the modeling process of the surrogate model in the sampling space of the original training data, which further accelerates the search process of the network in the NAS. Experimental results show that our proposed NAS framework can greatly reduce the search time, while the accuracy of the obtained network is comparable to that of other state-of-the art NAS algorithms.
On a conjecture on permutation rational functions over finite fields

Let p be a prime and n be a positive integer, and consider fb(X)=X+(Xp−X+b)−1∈Fpn(X), where b∈Fpn is such that Trpn/p(b)≠0. It is known that (i) fb permutes Fpn for p=2,3 and all n≥1; (ii) for p>3 and n=2, fb permutes Fp2 if and only if Trp2/p(b)=±1; and (iii) for p>3 and n≥5, fb does not permute Fpn. It has been conjectured that for p>3 and n=3,4, fb does not permute Fpn. We prove this conjecture for sufficiently large p.
GraphComm: Efficient Graph Convolutional Communication for Multiagent Cooperation

Artificial intelligence-empowered smart things (e.g., robots, autonomous vehicles, and unmanned aerial vehicles) have been transforming the world. The “brains” of smart things can be abstracted as the agents or cybertwins residing on end devices and edge servers. The next-generation communication networks (i.e., 6G) will become the nervous system for these agents and natively support multiagent cooperation. By sharing local observations and intentions via communication channels, the agents could better understand the environments and make right decisions. Due to the limited channel bandwidth, the communication is considered as a bottleneck of multiagent cooperation. In this article, we propose a graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck. Specifically, a variational information bottleneck is used to encode the observations and intentions compactly. Furthermore, a graph information bottleneck with the attention-based neighbor sampling mechanism is utilized to improve the effectiveness and robustness of the multiround communication process. The experimental results show that GraphComm can improve the effectiveness, robustness, and efficiency of communication in multiagent cooperative tasks as compared to baseline methods.
Construction of Substation Engineering Design Knowledge Graph Based on “Ontology Seven-step Method”

A great deal of substation engineering projects data and knowledge has been accumulated in the field of electric power survey and design. However, the lack of knowledge mining methods hinders its value creation. The knowledge management in the field hence needs a new mode. Knowledge graph has been widely adopted in some vertical industries recently. Since its schema-less nature, the knowledge graph could be constructed flexibly. It describes the complex relations between entities and concepts in the objective world. This study proposes the construction of substation engineering design domain knowledge graph. Ontology construction in this study is based on “ontology seven-step method” and in a top-down approach. Thematic clustering is considered to guide to construct ontology hierarchy manually. Concepts of substation engineering design are stored in the ontology layer, and entities from engineering projects information are in the instance layer. In this way, the structured knowledge can accelerate knowledge retrieving and assist in decision-making. Moreover, the tacit knowledge inferring can also promote knowledge innovation.
Block Chain-based access control and intrusion detection system in IoD

The Internet of Drones (IoD), a layered network, has attracted more attention among the research community due to its wide range of applications, such as packet transmission, rescue operations, traffic surveillance, and so on. Various drones known as Unmanned Aerial Vehicles (UAVs) are deployed in various flying zones to transmit important information to the Ground station server (GSS). Hence, secure transmission is the major challenging aspect of IoD. Hence, this paper introduces a novel technique for secure transmission in IoD by effective blockchain-assisted access control and intrusion detection approach using the newly devised Deep Neuro-Fuzzy Network system. Typically, Blockchain-based access control consists of mainly four phases, like pre-deployment, registration, authentication, and access control phase for transferring the crucial information in the IoD environment. Besides, intrusion in IoD is detected using the proposed Deep Neuro-Fuzzy Network. However, the proposed approach achieved a minimum delay of 1.253, a maximum precision rate of 88.375, and a maximum recall measure of 89.263 while considering the Neptune attack.
Stochastic Whitening Batch Normalization

Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton’s method. However, since Newton’s method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks.
Robust parameter estimation with outlier-contaminated correlated measurements and applications to aerodynamic coefficient identification

We consider the robust parameter estimation problem for state-space models with correlated measurements in the presence of outliers. Both the scenarios that the statistical information of the nominal noises in state-space models is known or not are explored. A novel Kalman robust smoother is proposed via introducing a specific reweighting approach to estimate the system parameters as well as the states when the nominal noise covariances are known. For the case where the statistic information of the nominal noise is absent, a modified expectation–maximization algorithm is introduced and integrated into the proposed robust smoother for simultaneously estimating the unknown states, system parameters, and the noise covariances. The performance of the proposed methods is illustrated by solving the aerodynamic coefficient identification problem with the real flight data set. The simulation results reveal that the proposed approaches outperform several existing solutions when outliers occur in multiple components of correlated measurements.
Integration of Blockchain with Connected and Autonomous Vehicles: Vision and Challenge

Connected and Autonomous Vehicles (CAVs) are introduced to improve individuals’ quality of life by offering a wide range of services. They collect a huge amount of data and exchange them with each other and the infrastructure. The collected data usually includes sensitive information about the users and the surrounding environment. Therefore, data security and privacy are among the main challenges in this industry. Blockchain, an emerging distributed ledger, has been considered by the research community as a potential solution for enhancing data security, integrity, and transparency in Intelligent Transportation Systems (ITS). However, despite the emphasis of governments on the transparency of personal data protection practices, CAV stakeholders have not been successful in communicating appropriate information with the end users regarding the procedure of collecting, storing, and processing their personal data, as well as the data ownership. This article provides a vision of the opportunities and challenges of adopting blockchain in ITS from the “data transparency” and “privacy” perspective. The main aim is to answer the following questions: (1) Considering the amount of personal data collected by the CAVs, such as location, how would the integration of blockchain technology affect transparency , fairness , and lawfulness of personal data processing concerning the data subjects (as this is one of the main principles in the existing data protection regulations)? (2) How can the trade-off between transparency and privacy be addressed in blockchain-based ITS use cases?
An Efficient and Secure Pairing Free Short ID-Based Signature Scheme over Elliptic Curve

The cost of pairing computation is much more than the cost of computing scalar multiplication over elliptic curve group. Hence, the bilinear pairing based digital signature are less practical in the applications where short signature length as well as fast signature generation and verification are required. For such type of applications, we present a pairing computation free efficient and secure short identity (ID)-based signature scheme over elliptic curve. The proposed signature scheme is unforgeable in ROM against existential forgery on adaptive chosen message and ID attack with the assumption that ECDLP is hard in G. Moreover, the proposed signature scheme uses general cryptographic hash function in spite of costly map-to-point hash function and the comparative performance analysis shows that the proposed scheme is much more efficient, secure and short in signature length from related ID-based signature schemes
ipmr: Flexible implementation of Integral Projection Models in R

Abstract Integral projection models (IPMs) are an important tool for studying the dynamics of populations structured by one or more continuous traits (e.g. size, height, body mass). Researchers use IPMs to investigate questions ranging from linking drivers to population dynamics, planning conservation and management strategies, and quantifying selective pressures in natural populations. The popularity of stage‐structured population models has been supported by R scripts and packages (e.g. IPMpack , popbio , popdemo , lefko3 ) aimed at ecologists, which have introduced a broad repertoire of functionality and outputs. However, pressing ecological, evolutionary and conservation biology topics require developing more complex IPMs, and considerably more expertise to implement them. Here, we introduce ipmr , a flexible R package for building, analysing and interpreting IPMs. The ipmr framework relies on the mathematical notation of the models to express them in code format. Additionally, this package decouples the model parameterization step from the model implementation step. The latter point substantially increases ipmr's flexibility to model complex life cycles and demographic processes. ipmr can handle a wide variety of models, including those that incorporate density dependence, discretely and continuously varying stochastic environments, and multiple continuous and/or discrete traits. ipmr can accommodate models with individuals cross‐classified by age and size. Furthermore, the package provides methods for demographic analyses (e.g. asymptotic and stochastic growth rates) and visualization (e.g. kernel plotting). ipmr is a flexible R package for integral projection models. The package substantially reduces the amount of time required to implement general IPMs. We also provide extensive documentation with six vignettes and help files, accessible from an R session and online.