Reviewerly/exHarmony huggingface dataset:
Token-based authentication for Hadoop platform

The Hadoop platform has been adopted to process Big Data. The existing authentication protocol for Hadoop is Kerberos. Kerberos protocol has a vulnerability against weak passwords, and it is not scalable. Many researchers propose new authentication protocols. The proposals have different problems such as vulnerability to attacks, unscalability or increased administrative burden, and Hadoop costs. This paper proposes an authentication protocol proper for Hadoop to address these limitations, which is called Hash Token Based Authentication(HTBA). HTBA develops a token-based authentication approach to achieve scalability. This paper has implemented HTBA, Kerberos, and Hash-chain protocols on the Deter lab environment to be able to conduct performance analysis. The performance study shows that HTBA reduces the token size impact on the authentication time. Finally, the formal security analysis is presented using a Syverson-van –Oorschot logic belief and AVISPA security verification tool and shows that HTBA is invulnerable and safe against attacks.
Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective

In “Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective”, the authors study a framework to construct new classes of stochastic estimators that can consistently beat existing benchmarks regardless of key model parameter values. Oftentimes biased estimators, such as finite-difference estimators in black box stochastic gradient estimation, require selection of tuning parameters to balance bias and variance and ultimately minimize overall errors. Unfortunately, this relies on model knowledge that is unknown a priori and thus leads to ad hoc choices in practice. The authors introduce a new notion called asymptotic minimax risk ratio, which is designed to compare new estimators against existing benchmarks, whose values less than one imply that the new estimators could asymptotically outperform the benchmarks regardless of the model parameter value. Based on this, the authors study an outperforming weighting scheme by explicitly analyzing the asymptotic minimax risk ratio via a tractable reformulation of a nonconvex optimization problem.
Synonym‐based multi‐keyword ranked search with secure k‐NN in 6G network

Abstract Sixth Generation (6G) integrates the next generation communication systems such as maritime, terrestrial, and aerial to offer robust network and massive device connectivity with ultra‐low latency requirement. The cutting edge technologies such as artificial intelligence, quantum machine learning, and millimetre enable hyper‐connectivity to extend the development of mobile communication. The potential technologies utilise different computing infrastructures, on‐demand software, platforms, and machine intelligence to access a massive amount of data in real‐time. The delivery of real‐time data demands third‐party remote access to exploit the resources such as network service and storage to meet user requirements. Moreover, certain computing infrastructures such as cloud, edge, and fog recommend privacy protection to guarantee effective data storage and information retrieval by a data centre. As a result, multi‐keyword searching over encrypted cloud data is commonly exercised to retrieve the cloud data with service integrity. With the use of mobile cloud computing, the data generating IoT device can easily outsource any kind of complex data management system from the local network system. It may commercialise the system site to achieve better resource‐saving. However, data privacy and protection are expected to ensure using encryption techniques before any sensitive data is outsourced via data generating IoT devices. Thus, this paper presents a synonym‐based multi‐keyword ranked search over encrypted cloud data in order to solve privacy‐preserving and computation cost. The proposed mechanism constructs an m‐Way search tree to allot an index vector for each document which applies a depth‐first search to compute top score ranking to realise better search efficiency. The examination results show that the assumed index vectors can be subdivided into the sub‐vectors before storing them into the index tree to achieve better security and computation efficiency.
Searchable Encryption Taxonomy: Survey

Cloud service providers allow businesses to drastically reduce their costs. However, it is good to know that most outsourced data is sensitive data. Therefore, to protect this data set from potential attacks on cloud servers, it is strongly recommended that you encrypt it before outsourcing. But cloud servers cannot search encrypted data by default. Many researchers have published work proposing efficient and secure schemes to overcome this problem. So far, many searchable encryption schemes have been published. Searchable Encryption has two main branches. 1-Symmetric searchable encryption (SSE) 2-Asymmetric searchable encryption (ASE). In this article, SE schemes are investigated in a new way. We provide a complete classification of searchable encryption schemes in terms of search type, index type, results type, security models, type of implementation, Multiplicity of users, Cryptographic Primitives, and Technique used. For each classification, the available schemes are compared. We provide an available searchable encryption solutions overview.
Exploring Phone-Based Authentication Vulnerabilities in Single Sign-On Systems

Phone-based authenticators (PBAs) are commonly incorporated into multi-factor authentication and passwordless login schemes for corporate networks and systems. These systems require users to prove that they possess a phone or phone number associated with an account. The out-of-band nature of PBAs and their security may not be well understood by users. Further, the frequency of PBA prompts may desensitize users and lead to increased susceptibility to phishing or social engineering. We explore such risks to PBAs by exploring PBA implementation options and two types of attacks. When employed with a real-world PBA system, we found the symptoms of such attacks were subtle. A subsequent user study revealed that none of our participants noticed the attack symptoms, highlighting the limitations and risks associated with PBAs.
BS: Blockwise Sieve Algorithm for Finding Short Vectors from Sublattices

The Shortest Vector Problem is a crucial part of the lattice theory and a central lattice problem in analyzing lattice-based cryptography. This work provides a new algorithm that finds a short vector by calling the sieve oracle in projected sublattices orthogonal to each other. We first propose the Block Sieve algorithm. With blockwise sieving, proper insertion and reduction, the coordinates of the right end of vector v can be recovered. The algorithm moves the block to recover the other coordinates. We continue to optimize the algorithm and propose the Progressive Block Sieve algorithm, employing techniques such as skipping to accelerate the procedure. In a d-dimensional lattice, smaller sieve calls in ( $$d-\varTheta ({d}/\ln {d})$$ )-dimensional sublattices are enough to find a short vector. We compare the experimental results on different lattices to test the performance of the new approach. On challenge lattices, our algorithm takes less time and fewer tours than original reduction algorithms to reach a similar outcome. As an application of the new algorithm, we test the performance of solving Learning With Errors problem. Our algorithm is able to solve the instances about 5% faster than sieving.
A convolutional neural network model of multi-scale feature fusion: MFF-Net

MFF-Net (a multi-scale feature fusion convolutional neural network) was designed to improve the recognition rate of handwritten digits. The low-level, middle-level and high-level features of the image were first extracted through the convolution operation, and then the low-level and intermediate features were further extracted through different convolutional layers, later directly fused with the high-level features of the image with a certain weight, and then processed by the full connection layer. By adding a batch normalization layer before the activation layer, and a dropout layer between the full connection layers, the accuracy and generalization capacity of the network are improved. At the same time, a dynamic learning rate algorithm was designed, with which, the trained network accuracy was significantly improved as shown in the experiments on the MNIST data set. The accurate rate could reach 99.66% through only 30 epochs training. The comparison indicated that the accuracy of the network model is significantly higher than that of others.
Anticipating measure synchronization in coupled Hamiltonian systems with machine learning

A model-free approach is proposed for anticipating the occurrence of measure synchronization in coupled Hamiltonian systems. Specifically, by the technique of parameter-aware reservoir computing in machine learning, we demonstrate that the machine trained by the time series of coupled Hamiltonian systems at a handful of coupling parameters is able to predict accurately not only the critical coupling for the occurrence of measure synchronization, but also the variation of the system order parameters around the transition point. The capability of the model-free technique in anticipating measure synchronization is exemplified in Hamiltonian systems of two coupled oscillators and also in a Hamiltonian system of three globally coupled oscillators where partial synchronization arises. The studies pave a way to the model-free, data-driven analysis of measure synchronization in large-size Hamiltonian systems.
Block by block – How Immutable Ledgers Drive Sustainability Efforts

Distributed ledger technology (DLT) like blockchain technology (BCT) is expected to have the power to improve sustainability in supply chains. To summarize and organize the insights of research in information systems (IS) on how BCT relates to transparency and, ultimately, sustainability of supply chains, this paper conducts a structured literature review. In total 46 articles have been identified and analyzed. The analysis shows that the potential of BCT to facilitate transparency in supply chains and thus enable sustainability measures stems from its decentralized and distributed nature. Further, BCT does not unfold this potential on its own; rather it is a technology that complements existing IS used to manage inter-organizational transactions. Overall, the paper contributes to the literature by providing a deeper insight into the possibilities, features, and merits of BCT in the SCM context, mainly focusing on transparency and sustainability matters. Summarizing and organizing the advantages of BCT and the shortcomings of existing IS used in SCM as discussed in prior research, this paper emphasizes that BCT needs to be considered as a complementary technology. Viewing BCT as such and implementing at such, BCT has great potential to support sustainability efforts significantly.
Data Management Platform For Smart Orchestration of Decentralized and Heterogeneous Vehicular Edge Networks

Intelligent edge orchestration has become a vital component within next generation communication networks, such as 5G. They offer optimal resource allocation and service distribution, hence allow for full utilization of the opportunities provided by those networks. Orchestrators make use of Machine Learning (ML) techniques to determine the most optimal operational decisions, such as deployment and scaling of services, ensuring the quality of the service performance. The training and validation of these models require significant amount of data. However, in such environments we deal with heterogeneous and distributed data sources, in which the data needs to be collected and pre-processed efficiently, and as such, made ready-to-use for these ML models. Hence, in this paper several state-of-the-art data management technologies suitable for edge computing and orchestration are investigated and compared. After investigating the theoretical features of these technologies, they are deployed and tested on the Smart Highway testbed. The strengths and shortcomings of these systems are presented and compared based on the Quality of Service (QoS) requirements for various vehicular services dealing with highly mobile users, i.e., vehicles, which are considered as quite stringent. This hybrid platform, combining several data management technologies, will be used to assist and enrich the research on smart edge orchestration at IDLab and serve as a reference for other interested parties.
A Decentralized Architecture for Dynamic and Federated Access Control Facilitating Smart Tourism Services

Edge computing and the Internet of Things (IoT) are inextricably linked, and much work has been devoted to strengthening their symbiotic relationship, providing better coverage and quality of service. These solutions are typically vertically tailored, provider-specific and bound to work within one administrative domain, as they presume direct deployment and ownership of the resources controlled in a centralised fashion. This model has effectively created a myriad of physically interconnected elements, logically broken down into separate domain-specific islands, each possibly applying different security/privacy policies, device and process control mechanisms, service access and provisioning schemes etc. To address this data and service balkanization phenomena, complex and time-consuming interactions between multiple providers are required to set up and operate federation of resources across domains and/or providers. Without loss of generality, we envision a scenario where stakeholders in a Smart Tourism ecosystem participate in resource federations to enrich their services and exploit complementarities. In this context, we present a decentralized architecture, relying on Distributed Ledger Technology (DLT), providing a flexible and effective federated access control mechanism to data and services.
Does digital technology adoption affect COVID-19 health outcomes? A quantitative analysis on the global level

Purpose Adopting digital technology could facilitate the public health response to the COVID-19 pandemic. Some analysts argue that countries that adopted digital technology in their health sector have managed to control the virus better (Whitelaw et al. , 2020). For instance, countries with more comprehensive contact tracing have significantly lower fatality rates (Yalaman et al. , 2021). Moreover, World Health Organization (WHO) believes this technology is a crucial enabler for countries to meet the current challenge (WHO. Regional Office for the Western Pacific &amp; University of Melbourne, 2021). In this regard, this study aims to quantitatively find the relationship between the technological advancement of countries and COVID-19 health outcomes, using seven technological indices that measure technological advancement. Design/methodology/approach The authors used the multiple linear regression method to answer the research questions. The first analysis focuses on a cross section of all countries worldwide, and the second focuses on European countries for which weekly death statistics exist after the pandemic. Findings The findings support those countries with more technological abilities managed to control the virus’s mortality better, as evidenced by the negative link between the mortality rate of COVID-19 and the technological factors at the national level. Results also reveal that technology adoption decreases the death risk due to COVID-19 in countries with more elderly people. The authors may argue that technological advancement positively correlates with the number of deaths and diagnosed cases because the authors can better collect data or because the virus spreads due to higher economic and business activities. However, such technological advancement significantly decreases the death risk (lower mortality rate in the first analysis and lower mortality rate for elderly people in the second analysis). Research limitations/implications Three important conclusions could be made from the results: a lower mortality rate is generally expected for countries adopting advanced technology; technological advancement significantly decreases the death risk for elderly people; and a higher technology adoption level does not necessarily result in fewer diagnosed cases of/death due to COVID-19. Originality/value Although some studies have focused on e-health applications in the public health response to the COVID-19 pandemic, no studies, to the best of the authors’ knowledge, have tried to quantify its efficacy, most especially on the global level.
EP-BERTGCN: A Simple but Effective Power Equipment Fault Recognition Method

With the advancement of China’s State Grid in recent years, text-based power equipment fault recognition has become an essential tool for power equipment maintenance. The task suffers from the domain gap that exists between the electric power domain and the general natural language processing domain. To improve the recognition performance, we proposed a method that combines pre-trained Bidirectional Encoder Representations from Transformers (BERT) and Graph Convolutional Network (GCN), i.e., Electric Power -BERTGCN. Our EP-BERTGCN first builds the graph among documents and words within documents based on pre-trained BERT. Then, the two softmax outputs with pre-trained BERT and GCNs are combined for final classification results. Extensive experiments show that our method outperforms previous baselines.
Rethinking Adversarial Examples in Wargames

Artificial intelligence technology is increasingly widely used in games, especially for wargames. The addition of artificial intelligence algorithms enables these games to solve decision-making problems in complex environments more quickly, surpassing the vast majority of experienced human players in competitive games. However, due to the vulnerability of the neural network before adversarial examples, all modules using artificial intelligence algorithms are at risk of being attacked. For wargames, adversarial examples will make the units in the game no longer able to follow the established routes or actions to perform tasks. Based on such risks, this paper proposes a deceptive concept scheme of attacking intelligent modules in wargames through adversarial examples, and proposes challenges and prospects for current technologies. To our knowledge, we are the first team to analyze the impact of adversarial examples in the running process of wargames, namely the OODA loop, and simulate them in the corresponding wargaming software. In the end, we found that when artificial intelligence technology is widely used in war games, adversarial examples will have a subversive impact on several activities in several steps, which will directly lead to the failure to complete the established game goals.
Learning to Ask Informative Sub-Questions for Visual Question Answering

VQA (Visual Question Answering) model tends to make incorrect inferences for questions that require reasoning over world knowledge. Recent study has shown that training VQA models with questions that provide lower-level perceptual information along with reasoning questions improves performance. Inspired by this, we propose a novel VQA model that generates questions to actively obtain auxiliary perceptual information useful for correct reasoning. Our model consists of a VQA model for answering questions, a Visual Question Generation (VQG) model for generating questions, and an Info-score model for estimating the amount of information the generated questions contain, which is useful in answering the original question. We train the VQG model to maximize the "informativeness" provided by the Info-score model to generate questions that contain as much information as possible, about the answer to the original question. Our experiments show that by inputting the generated questions and their answers as additional information to the VQA model, it can indeed predict the answer more correctly than the baseline model.
Blood Vessel Segmentation from Low-Contrast and Wide-Field Optical Microscopic Images of Cranial Window by Attention-Gate-Based Network

The stereomicroscope, which is an optical microscope, is used to observe the organoids cultured in cranial windows. A cranial window is a light accessible observation window made on the brain of mice through craniotomy. Organoids research is often conducted on cranial windows. Hence, the observation of blood vessels in them is important for organoid research, like organoid vascularization. Therefore, achieving a simple, low-cost method that extracts blood vessel structures would significantly help researchers observe the blood vessels in cranial windows from microscopic images. However, wide-field optical microscopic images taken by stereomicroscope suffer from low contrast and dura mater occlusion, complicating the observation of the blood vessels in such images. To address such problems and assist researchers who are observing vascular structures, we propose a method that segments blood vessels in cranial windows from low-contrast and wide-field microscopic images. Our method is based on the Attention U-Net framework and clDice, which considers the connectivity of blood vessels. In addition, for low-contrast and partial occlusion problems, we used contrast enhancement and dehazing as preprocessing steps. Our method achieved a Dice score of 75.56%, a clDice score of 79.95%, and the Accuracy of 91.41% on our microscopic image dataset, suggesting that our method can extract blood vessels from low-contrast and wide-field microscopic images better than other methods.
DA-AE: Disparity-Alleviation Auto-Encoder Towards Categorization of Heritage Images for Aggrandized 3D Reconstruction

In this paper, we propose DA-AE: Disparity Alleviation AutoEncoder for categorization of heritage images towards 3D reconstruction. Recent survey on preservation of heritage shows demand for the digitization and conservations of heritage sites owing to their susceptibility to natural disasters and human acts. Digital conservation can be facilitated via crowdsourcing of data useful for construction of 3D models. Data from multiple sites sourced may result in elimination of relevant images due to the limitations of the pipeline. Curation and categorization of the crowd-sourced data enables better 3D reconstruction. 3D reconstruction pipelines demand correlation between the data and also tries to eliminate the irrelevant information. The reconstruction pipeline is sensitive to selection of initial pair for reconstruction. By categorising individual sites, crowdsourced data can be used to create better 3D reconstructed models. Categorization of crowdsourced data demands learning robust representations of data. Towards this, we propose DA-AE for improved representation and categorization of data in latent space, along with a disparity alleviation loss. We demonstrate categorization as an event, with clustering as a downstream task. We compare our results of clustering with state-of-the-art methods on benchmark datasets (MNIST, FashionMNIST, and USPS). We demonstrate the effects of our categorization using custom dataset IDH10 and compare the results with state-of-the-art methods. We show a systematic and qualitative influence of the proposed method on 3D reconstruction of data.