Reviewerly/exHarmony huggingface dataset:
Security for the Metaverse: Blockchain and Machine Learning Techniques for Intrusion Detection

&lt;p&gt;The Metaverse, which is emerging as the next-generation (NextG) Internet, offers an immersive 3D virtual world in which people can organize various virtual activities and interact with each other seamlessly through digital avatars. As the NextG Internet, however, the Metaverse also faces severe security risks inherited from its predecessor as well as various new emerging threats. Furthermore, the decentralized nature of the Metaverse even makes it more challenging to mitigate these issues in a large-scale setting with numerous interactive wearable devices such as augmented, virtual reality (AR/VR) headsets and haptic devices. In this article, we aim to analyze the security aspect of the Metaverse thoroughly with special discussions of solutions enabled by blockchain and machine learning (ML). Firstly, we present a 4-layer architecture of the Metaverse and discuss potential solutions for Metaverse security based on blockchain and ML. Next, we develop a decentralized collaborative intrusion detection system (CIDS) based on blockchain and federated learning (FL) that allows such the Metaverse users to collaboratively protect this digital world, thereby solving the scalability and single-point-of-failure (SPoF) issues of traditional security approaches, which may not be effective in protecting the Metaverse with increasingly sophisticated attacks. Finally, we outline some key challenges and discuss future research directions for Metaverse security.&lt;/p&gt;
Measuring Data Re-Use Through Dataset Citations in OpenAlex

Open Data is a concept receiving increased attention and support in academic environments, with one justification being that shared data may be re-used in further research. But what evidence exists for such re-use, and what is the relationship between the producers of shared datasets and researchers making use of them? This work in progress uses dataset citations in the OpenAlex bibliometric database to analyze the relationship between creators of datasets and authors who cite them, at individual, institutional, and national levels. It finds that most countries cite US datasets more frequently than their own, and US-based institutions frequently make more use of other countries’ datasets than researchers producing citing works within those countries. It also finds a low rate of both self-citation and institution-matched citations for datasets. Understanding where and how the sharing of data between researchers, institutions, and countries takes place is essential to developing open research practices.
Extended-monitoring system：A new attempt in CAS’s research institute evaluation

How to adapt the construction of the evaluation system of research institutes of the Chinese Academy of Sciences to the new requirements? To address new challenges, we propose a new model with the core unchanged, while upgrading the traditional quantitative monitoring system to the Extended Monitoring system, thereby promoting the greater role of quantitative monitoring in the evaluation of the Institute.It can be said that the quantitative monitoring system and result expression method of the Extended Monitoring System have better achieved the dual purpose of monitoring and motivating scientific research institutions.
Broadening the conception of ‘what counts’ – example of a narrative CV in a university alliance

Research(er) assessment is currently under reform. For example, the CoARA initiative aims at building critical mass to change assessment systems to lean more on qualitative assessment instead of metrics. There are also articulated needs to recognise the diversification of academic careers and contributions in academic work. For example, despite the aims towards Open Science, assessment systems rarely take researchers’ contributions in Open Science into account. This paper is about a practical approach, which aims at helping universities to broaden the ways researchers are assessed, for example for the purposes of academic recruitment. It presents a narrative CV template covering diverse aspects of academic work. The paper is based on the work conducted in YUFERING, a joint project by the European university alliance YUFE.
Clustering experiments with the Astro benchmarking data set with semantic document embeddings – off-the-shelf vs. custom embeddings created from citations, text, and both

What accounts for the observed better quality of publication-level topical science clustering solutions which use only citation relations as input data, compared to those using sophisticated semantic similarity data derived from both citations and textual terms? A survey of empirical work relevant to the concept of unconscientious referencing practices indicates that purely citation-based methods should be affected by significant ‘citation noise’, unlike text-based methods. This study continues work with the Astro benchmarking data set for bibliometric clustering by applying semantic representation learning techniques to scientific documents in order to isolate the clustering performance difference between direct citations and textual terms. We investigate variants of Random Indexing embeddings learned on this data set and one pre-trained off-the-shelf semantic document embedding, SPECTER. The evaluation is performed with four previously introduced validation data sets but using a newly suggested clustering evaluation measure.
Comparing bibliographic descriptions in seven free-access databases

This communication aims to analyse the information that a large set of free-access databases (i.e., Crossref, Dimensions, Microsoft Academic, OpenAlex, Scilit, Semantic Scholar, The Lens) provides about indexed publications in their databases. Using a random sample of 116k publications from Crossref, each database was queried to retrieve the same document list with the purpose of comparing the metadata of their publications. The results show that the completeness degree is different between databases and that the search engines show more problems to extract abstracts and assign document typologies. Dimensions is the product that obtain the highest completeness percentages in abstracts, open access documents, bibliographic data and document types.
Verifiable Carbon Accounting in Supply Chains

&lt;p&gt;In face of the ongoing climate change, both reduction and offsetting of carbon emissions are critical. To this end, accurate, reliable emission data, and service-oriented architectures for processing the data are needed. Current carbon accounting practices, however, are often error-prone, costly, and time-consuming. Even in digital monitoring, reporting and verification (MRV) systems, the employment of single, trusted verification bodies inhibits transparent, fine-granular, and verifiable accounting on product instance-level in high-throughput supply chains. We propose Verifiable Carbon Accounting (VCA) as a novel accounting approach that leverages authenticity and zero-knowledge proofs in service-oriented architectures for creating non-disclosing emission reports that are peer-to-peer verifiable on blockchains. VCA builds upon and extends both conventional and digital MRV systems but ensures the confidentiality of business emission data and calculations while allowing for peer-to-peer transparency and verifiability. We introduce the concept and demonstrate VCA application for accounting product carbon footprints (PCFs) in supply chains. We present a proof-of-concept technical system design and implementation and discuss experimental findings, deriving both insights on VCA practicability and next steps. Overall, we show how VCA advances the state of art in carbon accounting in and beyond supply chains, and how VCA can serve as the basis for next-generation, accurate carbon accounting.&lt;/p&gt;
BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions

Class imbalanced problems (CIP) are one of the potential challenges in developing unbiased Machine Learning (ML) models for predictions. CIP occurs when data samples are not equally distributed between the two or multiple classes. Borderline-Synthetic Minority Oversampling Techniques (SMOTE) is one of the approaches that has been used to balance the imbalance data by oversampling the minor (limited) samples. One of the potential drawbacks of existing Borderline-SMOTE is that it focuses on the data samples that lay at the border point and gives more attention to the extreme observations, ultimately limiting the creation of more diverse data after oversampling, and that is the almost scenario for the most of the borderline-SMOTE based oversampling strategies. As an effect, marginalization occurs after oversampling. To address these issues, in this work, we propose a hybrid oversampling technique by combining the power of borderline SMOTE and Generative Adversarial Network to generate more diverse data that follow Gaussian distributions. We named it BSGAN and tested it on four highly imbalanced datasets: Ecoli, Wine quality, Yeast, and Abalone. Our preliminary computational results reveal that BSGAN outperformed existing borderline SMOTE and GAN-based oversampling techniques and created a more diverse dataset that follows normal distribution after oversampling effect.
Post Quantum Blockchain Security for the Internet of Things Survey and Research Directions

&lt;p&gt;This is a review of research challenges and opportunities for post-quantum blockchains in the IoT. &lt;/p&gt;
System-And-Software-Requirements-In-Relation-To-Observability-And-Explainability

&lt;p&gt;Software maintenance and evolution are crucial aspects of software development. In today's world, observability and explainability are becoming essential requirements for software systems. This research paper investigates the relationship between software maintenance and evolution with observability and explainability. The paper explores the importance of observability and explainability in software systems and how they impact the maintenance and evolution of software systems. The research paper presents various techniques and tools for achieving observability and explainability in software systems. The paper also highlights the challenges and future research directions in the field of software maintenance and evolution in relation to observability and explainability.&lt;/p&gt;
Research on Nonlinear Invariants of a Power Function over a Binary Field

Abstract The nonlinear invariant attack is a new and powerful cryptanalysis for lightweight block ciphers. The core step of such cryptanalysis is to find the nonlinear invariant(s) of its cascade round. Generally, for an n -bit width function, we need time complexity O (2 3n ) to find the nonlinear invariants. In this paper, we take consider of the power function x m over the finite field GF (2 n ), which is one of the most important cryptographic functions of last decades. Firstly, we study the nonlinear invariants of x m , we provide mathematical toolboxes named the ～ m periodical point and the ～ m equivalence class . Secondly, we present an algorithm to get all the nonlinear invariants of x m over GF (2 n ) at the price of time complexity O (2 n ). Moreover, if the growth of n exceeds our tolerance above, we also provide a method to get parts of nonlinear invariants of x m . Finally, we take consider of the nonlinear invariants of x 3 over GF (2 129 ) as application, which is used in the block cipher MiMC and seems impractical by existing methods. Our results allow us to find several (but not all) nontrivial nonlinear invariants of such function for the first time.
Mirages: On Anthropomorphism in Dialogue Systems

Automated dialogue or conversational systems are anthropomorphised by developers and personified by users. While a degree of anthropomorphism may be inevitable due to the choice of medium, conscious and unconscious design choices can guide users to personify such systems to varying degrees. Encouraging users to relate to automated systems as if they were human can lead to high risk scenarios caused by over-reliance on their outputs. As a result, natural language processing researchers have investigated the factors that induce personification and develop resources to mitigate such effects. However, these efforts are fragmented, and many aspects of anthropomorphism have yet to be explored. In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise, including reinforcing gender stereotypes and notions of acceptable language. We recommend that future efforts towards developing dialogue systems take particular care in their design, development, release, and description; and attend to the many linguistic cues that can elicit personification by users.
Diagnostic spatio-temporal transformer with faithful encoding

This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency. The key technical challenge is to extract actionable insights from the dependency tensor characterizing high-order interactions among temporal and spatial indices. We formalize the problem as supervised dependency discovery, where the ST dependency is learned as a side product of multivariate time-series classification. We show that temporal positional encoding used in existing ST transformer works has a serious limitation in capturing higher frequencies (short time scales). We propose a new positional encoding with a theoretical guarantee, based on discrete Fourier transform. We also propose a new ST dependency discovery framework, which can provide readily consumable diagnostic information in both spatial and temporal directions. Finally, we demonstrate the utility of the proposed model, DFStrans (Diagnostic Fourier-based Spatio-temporal Transformer), in a real industrial application of building elevator control.
Nonlinear optics as a source of high-dimensional genuine tripartite entanglement

We lay down a general scheme to quantify the amount of genuine tripartite entanglement present in the spatial and energy-time degrees of freedom of entangled photon triplets using a resource-based measure known as the tripartite entanglement of formation. Quantifying genuine tripartite entanglement relative to a number of maximally entangled three-qubit Greenberger-Horne-Zeilinger (GHZ) states called gebits, the tripartite entanglement of formation serves as a basis of comparison between different tripartite entangled states of differing dimension. Demonstrating genuine tripartite entanglement is doubly challenging because it is not enough to show that each party is inseparable from the other two (which is sufficient only for pure states). Instead, one must rule out all mixtures of all combinations of biseparable states from describing the tripartite state. To meet this challenge, we use entropic measures of the statistics of tripartite systems to simultaneously bound the correlations each party has with the other two, and in so doing place a lower limit to the tripartite entanglement of formation. Even though our measure never over-estimates the entanglement present, we estimate the effectiveness of our technique by determining the exact tripartite entanglement of a triple-gaussian triphoton wavefunction with the same correlations as seen in photon triplets generated in third-order spontaneous parametric down-conversion (SPDC). Between these two efforts, we show that a substantial amount of tripartite entanglement exists in both the spatial and energy-time degrees of freedom of these systems, and propose an experiment that can measure them.
Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning

Balancing exploration and exploitation has been an important problem in both game tree search and automated planning. However, while the problem has been extensively analyzed within the Multi-Armed Bandit (MAB) literature, the planning community has had limited success when attempting to apply those results. We show that a more detailed theoretical understanding of MAB literature helps improve existing planning algorithms that are based on Monte Carlo Tree Search (MCTS) / Trial Based Heuristic Tree Search (THTS). In particular, THTS uses UCB1 MAB algorithms in an ad hoc manner, as UCB1's theoretical requirement of fixed bounded support reward distributions is not satisfied within heuristic search for classical planning. The core issue lies in UCB1's lack of adaptations to the different scales of the rewards. We propose GreedyUCT-Normal, a MCTS/THTS algorithm with UCB1-Normal bandit for agile classical planning, which handles distributions with different scales by taking the reward variance into consideration, and resulted in an improved algorithmic performance (more plans found with less node expansions) that outperforms Greedy Best First Search and existing MCTS/THTS-based algorithms (GreedyUCT,GreedyUCT*).
Corrections to “Hybrid Signed Convolution Module With Unsigned Divide-and-Conquer Multiplier for Energy-Efficient STT-MRAM-Based AI Accelerator” [Jul 23 1078-1082]

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , the reference numbers [10], [11], [12], and [13] in <xref ref-type="table" rid="table2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table II</xref> were not correct and should be changed to [11], [12], [13], and [14]. The correct <xref ref-type="table" rid="table2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Table II</xref> is illustrated as follows.
Balancing Public Health and Personal Information Protection: Challenges and Crossroads of Health Code Apps in China (Preprint)

<sec> <title>BACKGROUND</title> Digital technologies, especially contact tracing apps, have been crucial in monitoring and tracing the transmission of COVID-19 worldwide, which presented challenges to personal information protection. In China, health code apps were implemented as an emergency response to the pandemic and planned for broader public health services. However, potential problems within privacy policies may compromise personal information protection under the Personal Information Protection Law (PIPL). </sec> <sec> <title>OBJECTIVE</title> Our objective is to evaluate the legal protection of personal information in the privacy policies of health code apps. </sec> <sec> <title>METHODS</title> This paper presents the first content analysis and quantitative research on privacy policies for health code apps in China, emphasizing the information processing justification and the informed consent. Using a mixed-methods approach, we reviewed regulatory documents and 28 available privacy policies. </sec> <sec> <title>RESULTS</title> Our findings reveal unsatisfactory compliance with PIPL, including the lack of specific purpose and sufficient necessity for processing personal information, inadequate notification and the absence of separate consent. </sec> <sec> <title>CONCLUSIONS</title> China should develop a balanced policy addressing both public health and personal information protection while facing the crossroads regarding the future of health code apps. The specific purpose of applying health code apps in public health services and the sufficient necessity for processing personal information should be reconsidered and articulated explicitly. Meanwhile, users’ informed consent must be obtained, including comprehensive notification and separate consent. </sec>
Generative Model-based Simulation of Driver Behavior when Using Control Input Interface for Teleoperated Driving in Unstructured Canyon Terrains

Unmanned ground vehicles (UGVs) in unstructured environments mostly operate through teleoperation. To enable stable teleoperated driving in unstructured environments, some research has suggested driver assistance and evaluation methods that involve user studies, which can be costly and require lots of time and effort. A simulation model-based approach has been proposed to complement the user study; however, the models on teleoperated driving do not account for unstructured environments. Our proposed solution involves simulation models of teleoperated driving for drivers that utilize a deep generative model. Initially, we build a teleoperated driving simulator to imitate unstructured environments based on previous research and collect driving data from drivers. Then, we design and implement the simulation models based on a conditional variational autoencoder (CVAE). Our evaluation results demonstrate that the proposed teleoperated driving model can generate data by simulating the driver appropriately in unstructured canyon terrains.
Entanglement-based Mutual Quantum Distance Bounding

Mutual distance bounding (DB) protocols enable two distrusting parties to establish an upper-bound on the distance between them. DB has been so far mainly considered in classical settings and for classical applications, especially in wireless settings, e.g., to prevent relay attacks in wireless authentication and access control systems, and for secure localization. While recent research has started exploring DB in quantum settings, all current quantum DB (QDB) protocols employ quantum-bits (qubits) in the rapid-bit exchange phase and only perform one-way DB. Specifically, the latest QDB proposals improve the initial ones by adding resistance to photon number splitting attacks, and improving round complexity by avoiding communication from the prover to the verifier in the last authentication phase. This paper presents two new QDB protocols that differ from previously proposed protocols in several aspects: (1) to the best of our knowledge, our protocols are the first to utilize entangled qubits in the rapid-bit exchange phase, previous protocols relied on sending individual qubits, not those from a pair of entangled ones; (2) our second protocol can perform mutual QDB between two parties in one execution, previous QDB protocols had to be executed twice with the prover and verifier roles reversed in each execution; (3) the use of entangled qubits in our protocols thwarts attacks that previous QDB protocols were prone to; (4) and finally, our protocols also eliminate the need for communication from the prover to the verifier in the last authentication phase, which was necessary in some previous QDB protocols. Our work paves the way for several interesting research directions which we briefly discuss in detail in the appendix.
Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy.