Reviewerly/exHarmony huggingface dataset:
Revisiting algebraic attacks on MinRank and on the rank decoding problem

The Rank Decoding problem (RD) is at the core of rank-based cryptography. Cryptosystems such as ROLLO and RQC, which made it to the second round of the NIST Post-Quantum Standardization Process, as well as the Durandal signature scheme, rely on it or its variants. This problem can also be seen as a structured version of MinRank, which is ubiquitous in multivariate cryptography. Recently, Bardet et al. (in: Canteaut and Ishai, Advances in cryptology—EUROCRYPT 2020, Springer, Cham, 2020; Advances in cryptology—ASIACRYPT 2020, international conference on the theory and application of cryptology and information security, 2020. Proceedings, 2020) proposed attacks based on two new algebraic modelings, namely the MaxMinors modeling which is specific to RD and the Support-Minors modeling which applies to MinRank in general. Both improved significantly the complexity of algebraic attacks on these two problems. In the case of RD and contrarily to what was believed up to now, these new attacks were shown to be able to outperform combinatorial attacks and this even for very small field sizes. However, we prove here that the analysis performed in Bardet et al. (in: Advances in cryptology—ASIACRYPT 2020, international conference on the theory and application of cryptology and information security, 2020. Proceedings, 2020) for one of these attacks which consists in mixing the MaxMinors modeling with the Support-Minors modeling to solve RD is too optimistic and leads to underestimate the overall complexity. This is done by exhibiting linear dependencies between these equations and by considering an $${{\mathbb {F}}}_{q^m}$$ version of these modelings which turns out to be instrumental for getting a better understanding of both systems. Moreover, by working over $${{\mathbb {F}}}_{q^m}$$ rather than over $${{\mathbb {F}}}_{q}$$ , we are able to drastically reduce the number of variables in the system and we (i) still keep enough algebraic equations to be able to solve the system, (ii) are able to analyze rigorously the complexity of our approach. This new approach may improve the older MaxMinors approach on RD from Bardet et al. (in: Canteaut and Ishai, Advances in cryptology—EUROCRYPT 2020, Springer, Cham, 2020; Advances in cryptology—ASIACRYPT 2020, international conference on the theory and application of cryptology and information security, 2020. Proceedings, 2020) for certain parameters. We also introduce a new hybrid approach on the Support-Minors system whose impact is much more general since it applies to any MinRank problem. This technique improves significantly the complexity of the Support-Minors approach for small to moderate field sizes.
Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

As a clean energy resource, coalbed methane (CBM) is an important industry in China’s dual-carbon strategic planning. Despite the immense potential of CBM resources in China, the current exploration level remains low due to outdated survey technology, impeding large-scale exploration and development. This study investigates the application of hyperspectral data in CBM enrichment areas, specifically focusing on the extraction of alteration minerals in the Hudi coal mine area of the Qinshui Basin using ZY-1 02D and Hyperion hyperspectral data. The hyperspectral alteration mineral identification methods are summarized and analyzed. A method that combines spectral feature matching and diagnostic characteristic parameters is proposed for mineral extraction based on the spectral characteristics of different minerals. The extraction results are verified through field samples using X-ray diffraction analysis. Results show that (1) both ZY-1 02D and Hyperion hyperspectral data yield favorable extraction results for clay and carbonate minerals; (2) the overall accuracy of clay and carbonate minerals extraction is higher using ZY-1 02D data compared with Hyperion data, with accuracies of 81.67% and 79.03%, respectively; (3) the proposed method effectively extracts alteration minerals in CBM enrichment areas using hyperspectral data, thereby providing valuable technical support for the application of hyperspectral data.
Human Activity Prediction Based on Forecasted IMU Activity Signals by Sequence-to-Sequence Deep Neural Networks

Human Activity Recognition (HAR) has gained significant attention due to its broad range of applications, such as healthcare, industrial work safety, activity assistance, and driver monitoring. Most prior HAR systems are based on recorded sensor data (i.e., past information) recognizing human activities. In fact, HAR works based on future sensor data to predict human activities are rare. Human Activity Prediction (HAP) can benefit in multiple applications, such as fall detection or exercise routines, to prevent injuries. This work presents a novel HAP system based on forecasted activity data of Inertial Measurement Units (IMU). Our HAP system consists of a deep learning forecaster of IMU activity signals and a deep learning classifier to recognize future activities. Our deep learning forecaster model is based on a Sequence-to-Sequence structure with attention and positional encoding layers. Then, a pre-trained deep learning Bi-LSTM classifier is used to classify future activities based on the forecasted IMU data. We have tested our HAP system for five daily activities with two tri-axial IMU sensors. The forecasted signals show an average correlation of 91.6% to the actual measured signals of the five activities. The proposed HAP system achieves an average accuracy of 97.96% in predicting future activities.
Role of virtual reality authentic experience on affective responses: moderating role virtual reality attachment

ABSTRACTABSTRACTThe aim of the study is to examine the role of virtual reality authentic experience on affective responses (enjoyment, emotional involvement, and a flow state) towards visit intention in virtual reality with moderation of virtual reality attachment, using the stimulus organism response framework. Purposive sampling was used to collect primary data from 273 Indian sample. Results revealed that virtual reality authentic experience had a strong impact on enjoyment, emotional involvement, and a flow state. Similarly, enjoyment, emotional involvement, and a flow state had a positive impact on the visit intention of the respondents, and virtual reality attachment had a strong moderation relationship between the enjoyment, emotional involvement, and a flow state and visit intention. This research provides insight into the motivations of would-be visitors to visit VR-presented locations, and it will be helpful for tourism agencies and businesses looking to increase visitor numbers via virtual reality.KEYWORDS: Virtual realityauthentic experienceenjoymentemotional involvementflow statevisit intention Disclosure statementNo potential conflict of interest was reported by the author(s).
Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes

Realizing the full potential of quantum computation requires quantum error correction (QEC), with most recent breakthrough demonstrations of QEC using the surface code. QEC codes use multiple noisy physical qubits to encode information in fewer logical qubits, enabling the identification of errors through a decoding process. This process increases the logical fidelity (or accuracy) making the computation more reliable. However, most fast (efficient run-time) decoders neglect important noise characteristics, thereby reducing their accuracy. In this work, we introduce decoders that are both fast and accurate, and can be used with a wide class of QEC codes including the surface code. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. Using the surface code threshold as a performance metric, we observe a threshold at 0.94% error probability for our decoders, outperforming the 0.82% threshold for a standard minimum-weight perfect matching decoder. We also test our belief-matching decoders in a theoretical case study of codes tailored to a biased noise model. We find that the decoders lead to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code. Surprisingly, in the well-below-threshold regime, the rectangular surface code becomes more resource efficient than the tailored surface code due to a previously unnoticed phenomenon that we call “fragile boundaries.” Our decoders outperform all other fast decoders in terms of threshold and accuracy, enabling better results in current quantum-error-correction experiments and opening up new areas for theoretical case studies.12 MoreReceived 8 June 2022Revised 5 June 2023Accepted 12 June 2023DOI:https://doi.org/10.1103/PhysRevX.13.031007Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionSurface code quantum computingQuantum Information
Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.
Drivers of Sustainable Supply Chain Management Using Internet of Things-Based Blockchain Technology

Information technologies are transforming conventional supply chain management (SCM) business practices and positively influencing sustainability. In particular, blockchain technology and the internet of things (IoT) are two important examples that play a significant role in sustainable supply chain management (SSCM). This chapter reviewed the impact of these technologies on SSCM separately and with their combination of forward and reversed supply chains. Academics and practitioners have studied the effects of these technologies on sustainability separately earlier, but integrating these technologies can lead to different insights and disclose several opportunities. Besides, the chapter highlights essential factors in developing blockchain and IoT to know the modern trends for industry automation, challenges, and future research opportunities on supply chain sustainability. Finally, the chapter presents an IoT-based service-oriented blockchain architecture for supply chain information systems that manifest the information-driven sustainability of the supply chain operation.
Identity-based proxy matchmaking encryption for cloud-based anonymous messaging systems

Cloud-based anonymous messaging systems have been widely utilized in covert conversations to enable people to anonymously exchange encrypted data in public using cryptographic techniques. Recently, Ateniese et al. proposed the identity-based matchmaking encryption (IB-ME) which provides confidentiality, anonymity and authenticity in a logical single step for such systems. However, there are two crucial and practical issues in IB-ME. On the one hand, the existing IB-MEs only achieve indistinguishability under adaptive chosen identity and plaintext attacks (IND-ID-CPA). How to structure the IB-ME with indistinguishability under adaptive chosen identity and ciphertext attacks (IND-ID-CCA) is still an open problem. On the other hand, the computational cost of the receiver is considerable when using IB-ME since the ciphertext leaks no information about its sender's and specified receiver's identities. Thus, the receiver has to attempt to match "all" ciphertexts one by one with its own decryption key and "all" potential senders' identities. To reduce such a cost of the receiver in IB-ME, it is desired for the receiver to delegate the decryption ability with respect to the potential senders to some proxies. With the proxy keys, these proxies can match the ciphertexts parallelly. Nonetheless, an obvious challenge is how to delegate such ability to a proxy without losing anonymity and authenticity to protect the privacy of both the sender and the receiver. In this paper, we propose a novel CCA-secure solution, called identity-based proxy matchmaking encryption (IB-PME) for cloud-based anonymous messaging systems, to address the foregoing issues. The security analysis and the performance evaluation demonstrate that our proposed IB-PME is highly practical.
Does Deplatforming Work?

Social media has become an outlet for extremists to fundraise and organize. While governments deliberate on how to regulate, some social media companies have removed creators of offensive content —deplatforming. I estimate the effects of deplatforming on revenue and viewership, using variation in the timing of removals across two video-streaming companies — YouTube, and its far-right competitor, Bitchute. Being deplatformed on Youtube results in a 30% increase in weekly Bitcoin revenue and a 50% increase in viewership on Bitchute. This increase in Bitchute activity is less than that on YouTube, meaning that deplatforming works in decreasing a content creator’s overall views and revenue.
‘What a waste of time’: An examination of cybersecurity legitimacy

Abstract Managers who oversee cybersecurity policies commonly rely on managerial encouragement (e.g., rewards) and employee characteristics (e.g., attitude) to drive compliant behaviour. However, whereas some cybersecurity initiatives are perceived as reasonable by employees, others are viewed as a ‘waste of time’. This research introduces employee judgements of cybersecurity legitimacy as a new angle for understanding employee compliance with cybersecurity policies over time. Drawing on theory from the organisational legitimacy and cybersecurity literature, we conduct a three‐wave survey of 529 employees and find that, for each separate wave, negative legitimacy judgements mediate the relationship between management support and compliance, as well as between cybersecurity inconvenience and compliance. Our results provide support for cybersecurity legitimacy as an important influence on employee compliance with cybersecurity initiatives. This is significant because it highlights to managers the importance of not simply expecting compliant employee behaviour to follow from the introduction of cybersecurity initiatives, but that employees need to be convinced that the initiatives are fair and reasonable. Interestingly, we did not find sufficient support for our expectation that the increased likelihood of a cybersecurity incident will moderate the legitimacy‐policy compliance relationship. This result suggests that the legitimacy perceptions of employees are unyielding to differences in the risk characteristics of the cybersecurity incidents facing organisations.
Risk identification and influence analysis model for urban energy internet based on knowledge graph improved decision-making trial and evaluation laboratory

The pressing issue of climate change has led to a growing concern for low-carbon energy transformation. Urban energy internet (UEI) provides a means to facilitate renewable energy consumption by leveraging modern energy grid, smart energy services, and cyber-physical systems. However, the implementation of UEI entails complex and unknown risks arising from energy supplies, cyber-attacks, and defective equipment. To address this challenge, we propose an improved decision-making trial and evaluation laboratory based on knowledge graph and complex network (KG-CN-DEMATEL). Specifically, we employ a knowledge graph with Gaussian embedding (KG2E) to vectorize text information related to the risks. The vectors are combined with experts' scores, which evaluate the importance and cohesion of the complex network. We then analyze the significance and interrelations of the risks by computing their reason and central degree. Our results demonstrate that (1) the improved model offers higher accuracy and convenience than the conventional DEMATEL, (2) the 15 risks are ranked based on their importance, with R1 (Renewable energy's uncertainty), R6 (Generation technology), and R2 (Cyber-physical degree) being the most significant, with central degrees of 2.913, 2.635, and 2.566, respectively, and (3) the risks can be classified into six causal and nine effect elements, with their interrelations analyzed.
Everything is varied: The surprising impact of instantial variation on ML reliability

Instantial variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process. Although taking into account IV is critical for the proper analysis of the results, this source of uncertainty and its impact on robustness have so far been neglected in Machine Learning (ML). To fill this gap, we look at how IV affects ML performance and generalization, and how its impact can be mitigated. Specifically, we provide a methodological contribution to formalize the problem of IV in the statistical learning framework. To prove the relevance of our contribution, we focus on one of the most critical domains, healthcare, and take individual (analytical and biological) variation as a specific kind of IV; in this domain, we use one of the largest real-world laboratory medicine datasets for the task of COVID-19 detection, to show that: (1) common state-of-the-art ML models are severely impacted by the presence of IV in data; and (2) advanced learning strategies, based on data augmentation and soft computing methods (data imprecisiation), and proper study designs can be effective at improving robustness to IV. Our findings demonstrate the critical relevance of correctly accounting for IV to enable safe deployment of ML in real-world settings.
Impact of Blockchain Technology on the Stock Market

Several of the inefficiencies and risks that afflict traditional stock market infrastructure have been addressed by blockchain technology. Blockchain, due to its decentralized and transparent nature, can provide a safe and efficient platform for investors and issuers to exchange assets without the need for middlemen. This chapter investigates the influence of blockchain technology on the stock market, examining the possible benefits and hazards connected with its implementation. The authors contend that blockchain has the potential to transform the stock market by lowering transaction costs, increasing liquidity, and improving transparency. Nevertheless, there are problems and hazards connected with integrating blockchain technology into the stock market, including regulatory and legal hurdles, interoperability concerns, and the possibility of market disruption. As blockchain technology evolves, market players must carefully assess the consequences of its adoption and work together toward its ethical and sustainable integration.
The effects of quantitative easing on Bitcoin prices

This study aimed to examine the impact of the US quantitative easing policy on Bitcoin Prices in 2017–2023. A Time-Varying Parameter VAR (TVP-VAR) model was constructed for discussing the price influence mechanisms of quantitative easing on Bitcoins. The study's empirical results showed that the impacts of quantitative easing policies on Bitcoin prices can vary over time, a temporary positive impact on Bitcoin prices through different channels was also observed. Furthermore, it was found to impose a long-term positive impact via the liquidity channel.
Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference

In this paper, we present Quark, an integer RISC-V vector processor specifically tailored for sub-byte DNN inference. Quark is implemented in GlobalFoundries' 22FDX FD-SOI technology. It is designed on top of Ara, an open-source 64-bit RISC-V vector processor. To accommodate sub-byte DNN inference, Quark extends Ara by adding specialized vector instructions to perform sub-byte quantized operations. We also remove the floating-point unit from Quarks' lanes and use the CVA6 RISC-V scalar core for the re-scaling operations that are required in quantized neural network inference. This makes each lane of Quark 2 times smaller and 1.9 times more power efficient compared to the ones of Ara. In this paper we show that Quark can run quantized models at sub-byte precision. Notably we show that for 1-bit and 2-bit quantized models, Quark can accelerate computation of Conv2d over various ranges of inputs and kernel sizes.
MEGA: Malleable Encryption Goes Awry

MEGA is a leading cloud storage platform with more than 250 million users and 1000 Petabytes of stored data. MEGA claims to offer user-controlled, end-to-end security. This is achieved by having all data encryption and decryption operations done on MEGA clients, under the control of keys that are only available to those clients. This is intended to protect MEGA users from attacks by MEGA itself, or by adversaries who have taken control of MEGA’s infrastructure.We provide a detailed analysis of MEGA’s use of cryptography in such a malicious server setting. We present five distinct attacks against MEGA, which together allow for a full compromise of the confidentiality of user files. Additionally, the integrity of user data is damaged to the extent that an attacker can insert malicious files of their choice which pass all authenticity checks of the client. We built proof-of-concept versions of all the attacks. Four of the five attacks are eminently practical. They have all been responsibly disclosed to MEGA and remediation is underway.Taken together, our attacks highlight significant shortcomings in MEGA’s cryptographic architecture. We present immediately deployable countermeasures, as well as longer-term recommendations. We also provide a broader discussion of the challenges of cryptographic deployment at massive scale under strong threat models.