Reviewerly/exHarmony huggingface dataset:
Trustworthy confidential virtual machines for the masses

Confidential computing alleviates the concerns of distrustful customers by removing the cloud provider from their trusted computing base and resolves their disincentive to migrate their workloads to the cloud. This is facilitated by new hardware extensions, like AMD's SEV Secure Nested Paging (SEV-SNP), which can run a whole virtual machine with confidentiality and integrity protection against a potentially malicious hypervisor owned by an untrusted cloud provider. However, the assurance of such protection to either the service providers deploying sensitive workloads or the end-users passing sensitive data to services requires sending proof to the interested parties. Service providers can retrieve such proof by performing remote attestation while end-users have typically no means to acquire this proof or validate its correctness and therefore have to rely on the trustworthiness of the service providers.
Pravega

The growing popularity of the data stream abstraction entails new challenging requirements when it comes to data ingestion and storage. Many organizations expect to retain data streams for extended periods of time and to store such stream data in a cost-effective manner. It is also crucial to reconcile apparently opposite properties, like data durability and consistency, along with high performance. Furthermore, data streams should not only deal with a high degree of parallelism, but also adapt to fluctuating workloads with little or no admin intervention. To our knowledge, no storage system for data streams fully copes with all these requirements.
Systematic Analysis of DDS Implementations

Publish-subscribe messaging is a popular communication paradigm in the (Industrial) Internet of Things, and the Data Distribution Service (DDS) is a well known standard for pub-sub communication middleware. Many vendor implementations of DDS exist, leaving users with the need to choose according to project and performance requirements. However, the wide range of parameters in DDS implementations not covered in the standard specification make this selection difficult and time-consuming. We present DDS-Perf, a novel and versatile cross-vendor benchmarking tool for performance analysis, and use it to provide data from studies on 4 popular DDS implementations (OpenDDS, RTI Connext, FastDDS and CycloneDDS) across a wide range of experimental setups. DDS-Perf allows us to provide a consistent methodology across all vendors, increasing fairness and comparability. Overall, we find that RTI Connext achieves the best all-round performance (exhibiting the best bandwidth and peak sample rate), while FastDDS (best end-to-end latency) and CycloneDDS also show promising results.
Sora

Fast response time for modern web services that include numerous distributed and lightweight microservices becomes increasingly important due to its business impact. While hardware-only resource scaling approaches (e.g., FIRM [47] and PARSLO [40]) have been proposed to mitigate response time fluctuations on critical microservices, the re-adaptation of soft resources (e.g., threads or connections) that control the concurrency of hardware resource usage has been largely ignored. This paper shows that the soft resource adaptation of critical microservices has a significant impact on system scalability because either under- or over-allocation of soft resources can lead to inefficient usage of underlying hardware resources. We present Sora, an intelligent, fast soft resource adaptation management framework for quickly identifying and adjusting the optimal concurrency level of critical microservices to mitigate service-level objective (SLO) violations. Sora leverages online fine-grained system metrics and the propagated deadline along the critical path of request execution to quickly and accurately provide optimal concurrency setting for critical microservices. Based on six real-world bursty workload traces and two representative microservices benchmarks (Sock Shop and Social Network), our experimental results show that Sora can effectively mitigate large response time fluctuations and reduce the 99th percentile latency by up to 2.5× compared to the hardware-only scaling strategy FIRM [47] and 1.5× to the state-of-the-art concurrency-aware system scaling strategy ConScale.
Fast, Light-weight, and Accurate Performance Evaluation using Representative Datacenter Behaviors

Datacenters rapidly evolve by adopting new features such as new hardware deployment and software patches. Adopting a new feature requires an accurate evaluation of its impact to minimize the risk to the multi-million dollar computing infrastructure. However, a comprehensive performance analysis of a datacenter is extremely challenging due to its cost and multitenancy. Evaluating the performance in a live datacenter is accurate but prohibitive to prevent any damage to production services. Using conventional load-testing benchmarks on small-scale testbeds is imprecise as they do not consider the effect of other co-located jobs.
ttPAKE: Typo tolerance password-authenticated key exchange

Error tolerant password-authenticated key exchange (PAKE) allows a user to authenticate to a server using a password and agree on a session key with the server, provided that the password for authentication and the registered one are "close enough". Recently, an ingenious error tolerant PAKE scheme (faPAKE) for generalized passwords (e.g., fingerprints) emerged. In this paper, inspired by the mechanism of faPAKE, we propose a typo tolerance password-authenticated key exchange scheme, dubbed ttPAKE, for ordinary passwords. In ttPAKE, we introduce double-layered secret sharing (DLSS) that splits a main secret into shares and further splits the shares into sub-shares. DLSS can be considered homomorphic to typo tolerance of a password and would be an effective approach to achieve typo tolerance of the password without direct operations on the password for security requirements. A table is built to associate DLSS with typo tolerance of the password. The password is converted into position information for DLSS to store the sub-shares in the table. The table is scrambled with random numbers, and hence the information of the password is concealed. Using DLSS, our scheme implicitly determines whether typos in a password for authentication is no more than a threshold, which accomplishes typo tolerance while protecting passwords. We demonstrate that ttPAKE is secure under the Universal Composability model with security proofs. The performance evaluation shows that ttPAKE is efficient in terms of computation, communication, and storage costs.
LO

Manipulation of user transactions by miners in permissionless blockchain systems is a growing concern. This problem is a pervasive and systemic issue that incurs high costs for users of decentralised applications and is known as Miner Extractable Value (MEV). Furthermore, transaction manipulations create other issues such as congestion, higher fees, and system instability. Detecting transaction manipulations is difficult, even though it is known that they originate from the pre-consensus phase of transaction selection for building blocks, at the base layer of blockchain protocols. In this paper, we summarize known transaction manipulation attacks. We present LO, an accountable base layer protocol designed to detect and mitigate transaction manipulations. LO is built around the accurate detection of transaction manipulations and assignment of blame at the granularity of a single mining node. LO forces miners to log all the transactions they receive into a secure mempool data structure and to process them in a verifiable manner. Overall, LO quickly and efficiently detects censorship, injection or re-ordering attempts. Our performance evaluation shows that LO is also practical and only introduces a marginal performance overhead.
SinClave

For trusted execution environments (TEEs), remote attestation permits establishing trust in software executed on a remote host. It requires that the measurement of a remote TEE is both complete and fresh: We need to measure all aspects that might determine the behavior of an application, and this measurement has to be reasonably fresh. Performing measurements only at the start of a TEE simplifies the attestation but enables "reuse" attacks of enclaves. We demonstrate how to perform such reuse attacks for different TEE frameworks. We also show how to address this issue by enforcing freshness -- through the concept of a singleton enclave -- and completeness of the measurements. Completeness of measurements is not trivial since the secrets provisioned to an enclave and the content of the filesystem can both affect the behavior of the software, i.e., can be used to mount reuse attacks. We present mechanisms to include measurements of these two components in the remote attestation. Our evaluation based on real-world applications shows that our approach incurs only negligible overhead ranging from 1.03% to 13.2%.
DynaCut

Software is becoming increasingly complex and feature-rich, yet only part of any given codebase is frequently used. Existing software customization and debloating approaches target static binaries, focusing on feature discovery, control-flow analysis, and binary rewriting. As a result, the customized program binary has a smaller attack surface as well as less available functionality. This means that once a software's use scenario changes, the customized binary may not be usable.
Glider

Serverless data analytics generate a large amount of intermediate data during computation stages. However, serverless functions, which are short-lived and lack direct communication, face significant challenges in managing this data effectively. The traditional approach of using object storage to carry the data proves to be slow and costly, as it involves constant movement of data back and forth. Although specialized ephemeral storage solutions have been developed to address this issue, they fail to tackle the fundamental challenge of minimizing data movements. This work focuses on incorporating near-data computation into an ephemeral storage system to reduce the volume of transferred data in serverless analytics. We present Glider with the aim to enhance communication between serverless compute stages, allowing data to smoothly "glide" through the processing pipeline instead of bouncing between different services. Glider achieves this by leveraging stateful near-data execution of complex data-bound operations and an efficient I/O streaming interface. Under evaluation, it reduces data transfers by up to 99.7%, improves storage utilization by up to 99.8%, and enhances performance by up to 2.7×. In sum, Glider improves serverless data analytics by optimizing data movement, streamlining processing, and avoiding redundant transfers.
Renovation sets and their applications in higher-order argumentation frameworks

Abstract The acceptability of attacks plays a key role in defining semantics of higher-order argumentation frameworks (HO-AF). Several approaches have been put forward to describe the recursive acceptability of attacks. The diversity, on the one hand, leads to the variety of HO-AF semantics. However, on the other hand, it also brings difficulties in the formal examination of the relationship among these distinct semantics and the exploration of new semantics. This work aims at exploring the underlying commons in existing HO-AF semantics and examining the role of such commons in the study of HO-AF semantics. To this end, we introduce novel notion of renovation sets to represent the acceptability of attacks, and identify several types of renovation sets for different kinds of acceptability. We then use renovation sets to represent and compare HO-AF semantics built in four different approaches. The proposal of renovation sets provides a general tool for the study of HO-AFs. The representation demonstrates a method of using renovation sets to establish HO-AF semantics. The comparison shows the feasibility of investigating the relationship between different HO-AF semantics with the help of renovation sets.
Re-structuring Curriculum in Tertiary Institutions to Drive Sustainable Eco-civilization in China

Abstract In the strategic context of building a comprehensive ecologically civilized society as envisaged by the Communist Party and State Government of China, Eco-civilization education would become a mandatory new mission of the higher educational institutions in China. Linking the constructive postmodernism and ecological Marxism theories to eco-civilization, the purpose of the present essay is to restructure the higher education system for realizing the eco-civilized society in China via the literature integration methodology, the findings are as follows: (1) the goal of instituting the educational system of Eco-civilization in Chinese universities and colleges is to facilitate the culturing ecological literacy of the graduates; (2) the implementation of Eco-civilization education will create the graduates’ awareness, impart their requisite knowledge, and motivate them to adopt practices that promote Eco-civilization; and (3) the triple transitions of the higher education institutions in China to deliver on their missions as implementers of Eco-civilization education. Firstly, they must roll-out pragmatic programs to enhance students’ Eco-civilization conscious through the teaching Eco-civilization concepts as part of ideological and political courses. Secondly, they could also deepen students’ Eco-civilization knowledge by adjusting the teaching content and pedagogy of ecology courses. Finally, they could facilitate the Eco-civilization process by reinforcing behavioral reforms required to drive sustainable Eco-civilization and help positions these students as devoted ambassadors of Eco-civilization after graduation.
Geoarchaeological investigations in Artanish Peninsula, Armenia: Testing a new geochemical prospecting method for archaeology

Abstract Within the framework of an Armenian–German research project, taking place between 2019 and 2021 on the Artanish Peninsula at Lake Sevan (Armenia), in addition to numerous (geo‐) archaeological investigations, methods of geochemical prospection have been carried out. The ancient burial grounds of Artanish 23 and Artanish 29 have served as model sites to successfully test the well‐known method of geochemical prospection and evaluation of metal deposits in geology (mineral sector). As a result, a new experimental archaeo‐geochemical prospecting and evaluation method has been developed, which has been adapted for the exploration of archaeological monuments. It is planned to use this experimental method (which we consider new and important in archaeogeochemical investigation, but not a conclusive and comprehensive work per se), which has already proven its work capacity, in archaeological research, in the prospective areas of Armenia and other countries. In addition to these investigations, research on the transformations in the landscape of the ancient tombs related to Lake Sevan fluctuations has also been carried out. The anthropogenic impact of humans on the environment (geochemical halos formed in the soil on the surface of the tombs) has been studied, as well as the problem of the impact of geological environment on human life activities, that is, the relocation of the burial grounds to more elevated areas due to the rise in the lake level. Based on the results of geochemical sampling and high‐resolution magnetometer surveys, excavations have been performed at the site. Here, we present the results of an experimental study exploring the potential of combined magnetometer prospection and chemical soil analyses to locate and characterize the burial ground of Artanish on Lake Sevan, Armenia. The results have demonstrated the capability of these analyses to detect the sites, outline hotspots and interpret the features identified in the magnetometer results.
Radar emitter structure identification based on stacked frequency sparse auto‐encoder network

Abstract In the current complex situations of electronic intelligence (ELINT), the authors present a radar emitter structure (RES) identification method based on deep learning at a new level to address the issue of incomplete cognitive information. Firstly, due to the fact that existing simulation data cannot fully reflect the structure features of the entire radar emitter, the structure feature‐level RES model is built using direct digital synthesiser (DDS) technology and radio frequency (RF) simulation platform. Afterwards, considering that the structure features are reflected in the frequency domain, a stacked frequency sparse auto encoder (sFSAE) network is constructed by adding a constraint term in frequency domain to the loss function of sparse auto encoder (SAE). Using deep learning to extract structure features with constraints in different domains is instructive for feature extraction techniques under variable operating parameters. Finally, the extracted structure features are input into the Softmax classifier to perform the identification from the radar signal to the RES. The experimental results show that the proposed method has high generalisation ability and robustness under different modulation types, different operating parameters and different signal to noise ratio (SNR). It also has a high identification rate even for untrained modulated signals.
Blockchain and the transition to the circular economy: A literature review

Abstract Companies and industries face various challenges when it comes to embracing the circular economy (CE). These challenges not only deter companies from adopting effective strategies that promote it but also hinder industries from effectively establishing a circular system that ensures the addition of economic value to the supply chain. To address these challenges, blockchain technology has emerged as a new and revolutionary protocol for sharing and updating information. It does so by linking databases within a decentralized, peer‐to‐peer, open‐access network, guaranteeing secure, immutable, and irreversible operations. Within this context, the primary objective of this article is to investigate how blockchain technology supports the transition to the CE. Additionally, the paper aims to identify the barriers that hinder the process of transitioning to the CE. To achieve this, a systematic literature review (SLR) was conducted, considering 22 articles published in recent years across Scopus, Web of Science, and Science Direct databases. The results of this review underscore the considerable potential of blockchain in supporting companies and industries as they embrace their roles in the CE and seek to overcome the barriers they encounter. The findings reveal that blockchain supports the transition to the CE by facilitating decentralization, enabling smart contracting, enhancing information sharing and improvement, ensuring traceability and transparency, providing incentives, offering protection and security, and promoting value creation. However, in examining the barriers that blockchain may face in supporting companies and industries as they transition to the CE, certain challenges are highlighted. These include issues related to low scalability, insufficient funding, underdeveloped regulations, limited infrastructure support, a lack of standardization and generalization, high initial adoption costs, associated risks, and a deficiency in engagement and awareness. In conclusion, this study contributes to the existing literature by demonstrating that although barriers may pose challenges to blockchain's role in facilitating the CE transition, the benefits it offers are likely to outweigh these obstacles.
A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection

Abstract Due to the robustness of skeleton data to human scale, illumination changes, dynamic camera views, and complex backgrounds, great progress has been made in skeleton‐based video anomaly detection in recent years. The spatio‐temporal graph convolutional network has been proven to be effective in modelling the spatio‐temporal dependencies of non‐Euclidean data such as human skeleton graphs, and the autoencoder based on this basic unit is widely used to model sequence features. However, due to the limitations of the convolution kernel, the model cannot capture the correlation between non‐adjacent joints, and it is difficult to deal with long‐term sequences, resulting in an insufficient understanding of behaviour. To address this issue, this paper applies the Transformer to the human skeleton and proposes the Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder (STEGT‐AE) to improve the capability of modelling. In addition, the multi‐memory model with skip connections is employed to provide different levels of coding features, thereby enhancing the ability of the model to distinguish similar heterogeneous behaviours. Furthermore, the STEGT‐AE has a single encoder‐double decoder architecture, which can improve the detection performance by the combining reconstruction and prediction error. The experimental results show that performances of STEGT‐AE is significantly better than other advanced algorithms on four baseline datasets.
Adaptive kernel density estimation weighted twin support vector machine and its sample screening method

Summary In twin support vector machines (TSVM), noise blurs the boundary between positive and negative samples, increasing the probability of classification errors. In this article, we propose an adaptive kernel density estimation weighted twin support vector machine(AKWTSVM). AKWTSVM uses KDE based on K‐nearest neighbor estimation to calculate the probability density of samples. It automatically selects the optimal bandwidth based on the local density of the samples to improve the robustness of the algorithm. However, TSVM has high time complexity, to reduce the time costs, a sample screening method is proposed for AKWTSVM, named AKWTSVM‐SSM, which is based on the overall distance and local density, and reduces the time costs of the algorithm by reducing the sample size while ensuring the accuracy of the algorithm. The experiment with differently scaled noise environments of 0%, 5%, 10%, 15%, and 20% on 12 UCI datasets validate the accuracy and running time of AKWTSVM and AKWTSVM‐SSM. Experimental results prove the effectiveness and robustness of AKWTSVM, the robustness of AKWTSVM‐SSM, and its applicability to large‐scale datasets.
A Novel Learning Approach to Remove Oscillations in First‐Order Takagi–Sugeno Fuzzy System: Gradient Descent‐Based Neuro‐Fuzzy Algorithm Using Smoothing Group Lasso Regularization

Abstract As a universal approximator, the first order Takagi–Sugeno fuzzy system possesses the capability to approximate widespread nonlinear systems through a group of IF THEN fuzzy rules. Although group lasso regularization has the advantage of inducing group sparsity and handling variable selection issues, it can lead to numerical oscillations and theoretical challenges in calculating the gradient at the origin when employed directly during training. The paper addresses the aforementioned obstacle by invoking a smoothing function to approximate group lasso regularization. On this basis, a gradient‐based neuro fuzzy learning algorithm with smoothing group lasso regularization for the first order Takagi–Sugeno fuzzy system is proposed. The convergence of the proposed algorithm is rigorously proved under gentle conditions. In addition, experimental outcomes acquired on two approximations and two classification simulations demonstrate that the proposed algorithm outperforms the algorithm with original group lasso regularization and L 2 regularization in terms of error, pruned neurons, and accuracy. This is particularly evident in significant advancements in pruned neurons due to group sparsity. In comparison to the algorithm with L 2 regularization, the proposed algorithm exhibits improvements of 6.3, 5.3, and 142.6 in pruned neurons during sin function, Gabor function, and Sonar benchmark dataset simulations, respectively.