Reviewerly/exHarmony huggingface dataset:
Modularized Pre-Training for End-to-End Task-Oriented Dialogue

Pre-training for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b> nd-to-end <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</b> ask- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> riented <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</b> ialogue <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> ystems (EToDs) is a challenging task due to its unique knowledge base query (accuracy) need and lack of sufficient training data (fluency). In this paper, we try to mitigate the above challenges by introducing a modularized pre-training framework for EToDs, which achieves to effectively improve both accuracy and fluency of EToDs through a pre-training paradigm. The core insight is a modular design by decomposing EToDs into a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generation (fluency)</i> module and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge-retriever (accuracy)</i> module, which allows us to optimize each module by pre-training these two sub-modules with different well-designed pre-training tasks, respectively. In addition, such a modularized paradigm enables us to make full use of large amounts of KB-free dialogue corpus for the pre-training <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generation</i> module, which can alleviate the insufficient training problem. Furthermore, we introduce a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency-guided</i> data augmentation (CGDA) strategy to cope with the data scarcity problem to better pre-train the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge-retriever</i> module. Finally, we fine-tune the pre-trained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generation</i> module and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge-retriever</i> module jointly. Experimental results on three datasets show that our model achieve superior performance in terms of both fluency and accuracy. To our knowledge, this is the first work to explore modularized pre-training methods for EToDs.
World State Attack to Blockchain Based IoV and Efficient Protection With Hybrid RSUs Architecture

Blockchain technology is developing rapidly and has been widely applied in the field of Internet of Vehicles (IoV) to solve trust and security problems. However, due to the high security requirements in IoV scenarios, the security threats of blockchain itself become a big challenge for its applications in IoV. As the largest distributed platform supporting smart contract, Ethereum becomes one of the popular blockchain platforms that has been applied in IoV applications. In Ethereum, the local world state (stored on Road Side Units (RSUs) in IoV) is applied to facilitate account query and transaction verification. However, previous works showed that the local database can be easily tampered, so attackers may issue invalid transactions based on the modified world state, which is not acceptable for IoV applications. In this paper, the success probability and expected time for such an attack are first analyzed theoretically, including the effect of portion of tampered RSUs and the number of required confirmation blocks. Then experiment evaluation verifies the correctness of the theoretical analysis and shows that the attack would succeed with a higher probability within a shorter time when the local database on more RSUs are attacked. On the contrary, increasing of confirmation blocks can effectively reduce the success probability of a single attack and extend the confirmation time of the invalid transaction. Finally, efficient attack detection and recovery methods are proposed based on a novel hierarchical architecture with hybrid RSUs, and the effectiveness and complexity are verified by theoretical analysis and experiments.
Predicting the prognosis of HER2-positive breast cancer patients by fusing pathological whole slide images and clinical features using multiple instance learning

In 2022, breast cancer will become an important factor affecting women's public health and HER2 positivity for approximately 15-20$ \% $ invasive breast cancer cases. Follow-up data for HER2-positive patients are rare, and research on prognosis and auxiliary diagnosis is still limited. In light of the findings obtained from the analysis of clinical features, we have developed a novel multiple instance learning (MIL) fusion model that integrates hematoxylin-eosin (HE) pathological images and clinical features to accurately predict the prognostic risk of patients. Specifically, we segmented the HE pathology images of patients into patches, clustered them by K-means, aggregated them into a bag feature-level representation through graph attention networks (GATs) and multihead attention networks, and fused them with clinical features to predict the prognosis of patients. We divided West China Hospital (WCH) patients (n = 1069) into a training cohort and internal validation cohort and used The Cancer Genome Atlas (TCGA) patients (n = 160) as an external test cohort. The 3-fold average C-index of the proposed OS-based model was 0.668, the C-index of the WCH test set was 0.765, and the C-index of the TCGA independent test set was 0.726. By plotting the Kaplan-Meier curve, the fusion feature (P = 0.034) model distinguished high- and low-risk groups more accurately than clinical features (P = 0.19). The MIL model can directly analyze a large number of unlabeled pathological images, and the multimodal model is more accurate than the unimodal models in predicting Her2-positive breast cancer prognosis based on large amounts of data.
Raw or Cooked? Object Detection on RAW Images

Images fed to a deep neural network have in general undergone several handcrafted image signal processing (ISP) operations, all of which have been optimized to produce visually pleasing images. In this work, we investigate the hypothesis that the intermediate representation of visually pleasing images is sub-optimal for downstream computer vision tasks compared to the RAW image representation. We suggest that the operations of the ISP instead should be optimized towards the end task, by learning the parameters of the operations jointly during training. We extend previous works on this topic and propose a new learnable operation that enables an object detector to achieve superior performance when compared to both previous works and traditional RGB images. In experiments on the open PASCALRAW dataset, we empirically confirm our hypothesis.
Scenario-Based Foresight in the Age of Digital Technologies and AI

Abstract Scenario-based foresight is used less and less in the corporate world despite continued high satisfaction with the obtained results. In the age of digitalization, many companies feel increasingly forced to short-termism instead of strategic planning. However, emerging digital technologies, such as artificial intelligence (AI), represent a promising approach to cope with the traditional challenges of scenario-based foresight as well as new challenges added by digitalization. Therefore, this work-in-progress paper identifies and analyzes use cases for scenario-based foresight with digital technologies employing a systematic analysis of the relevant literature. In the paper at hand, we show that the use of digital technologies for improving the performance of scenario-based foresight is an emerging field. We identify 14 so-called use cases, i.e., unique goal-oriented applications of digital technologies for scenario-based foresight. In general, the use cases show that currently digital technologies can enhance, not substitute the capabilities of scenario-based foresight practitioners. Digital technologies primarily support the analysis of large amounts of data, e.g., for collecting futuristic data and identifying key influence factors. However, activities that require implicit knowledge and creativity, like the interpretation of scenarios, are currently still left to humans.
Quantification of Uncertainties in Neural Networks

Abstract Artificial neural networks only compute point estimates and thus, do not provide the user with a proper decision space. In high-risk use cases, the confidence of the neural network is an important support for decision-making. Bayesian neural networks extend classical deep neural networks with a probability component and allow the user to assess the probability distribution over the prediction. Due to the large number of parameters to be learned, the calculation of the predictive probability can only be performed approximately in practice. In recent years, many methods have been developed to efficiently learn the parameter distributions for Bayesian neural networks. Each of these has different advantages and disadvantages, and thus can be used for different applications. Quantifying uncertainty in the context of neural networks allows the user to interpret the results more comprehensively as well as to assess the risk and therefore makes an important contribution to the user’s digital sovereignty.
Debasing Language Expressed by Two Radical Right-Wing Populist Leaders in the Netherlands: Geert Wilders and Thierry Baudet

Far-right political movements are on the rise internationally, and part of their success might be due to their leaders' online communication. These leaders may appeal to the sentiments of disenchanted citizens by using debasing language targeting elites and societal out-groups. Additionally, they can exploit the affordances of social media to reach their constituents without the interference of gatekeepers that condemn debasing language. To test the assumption that radical right-wing politicians use debasing speech in their social media communication, this chapter reports on a qualitative analysis of the Twitter communication of Dutch political leaders Thierry Baudet and Geert Wilders. The findings indicate that both leaders use debasing language when referring to political elites and mainstream media. Wilders relies on a more explicit debasing vocabulary, and uses swearing, cursing and degrading language—including dehumanization—when referring to political opponents and religious or ethnic minorities. Baudet uses less uncivil forms of debasement, although his language frequently devaluates opponents in politics and media. Based on these findings, we can conclude that debasing language can be used to construct populist opposition to elites and out-groups. However, there are different degrees of debasement, and only Geert Wilders explicitly uses hate-driven language and swearing when excluding others.
Federated Learning Over-the-Air by Retransmissions

Motivated by the increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL) is of particular interest due to its communication efficiency and its ability to deal with the problem of non-IID data. FL training can be accelerated by a wireless communication method called Over-the-Air Computation (AirComp) which harnesses the interference of simultaneous uplink transmissions to efficiently aggregate model updates. However, since AirComp utilizes analog communication, it introduces inevitable estimation errors. In this paper, we study the impact of such estimation errors on the convergence of FL and propose retransmissions as a method to improve FL accuracy over resource-constrained wireless networks. First, we derive the optimal AirComp power control scheme with retransmissions over static channels. Then, we investigate the performance of Over-the-Air FL with retransmissions and find two upper bounds on the FL loss function. Numerical results demonstrate that the power control scheme offers significant reductions in mean squared error. Additionally, we provide simulation results on MNIST classification with a deep neural network that reveals significant improvements in classification accuracy for low-SNR scenarios.
Decentralized Global Copyright System Based on Consortium Blockchain With Proof of Authority

Conventional copyright systems are governed nationally, and there is no global ledger for storing copyright data. Due to the lack of a global copyright monitoring system, it is difficult to provide cross-border copyright protection. In this paper, we propose a novel decentralized copyright system based on a consortium blockchain, which ensures cross-border copyright protection of individuals’ digital content and solves existing challenges in international copyright management. The proposed system enables a synchronized platform to register and trade copyright globally without using a global cloud. Individual countries receive membership from a copyright federation and participate in block creation by executing the energy-efficient proof of authority consensus algorithm. These countries are regarded as the authorities of the platform. They validate transactions conducted by users and store them in the blockchain. Anyone, either registered or unregistered, can investigate a copyrighted work, but only registered users can make transactions. A token-based payment method is also proposed for paying copyright charges (i.e., transaction fees) to authorities through the federation. A prototype of the system was implemented, and its performance was evaluated. This paper provides direction and guidance towards international copyright management.
Non-Parallel Whisper-to-Normal Speaking Style Conversion Using Auxiliary Classifier Variational Autoencoder

This paper is concerned with non-parallel whisper-to-normal speaking-style conversion (W2N-SC), which converts whispered speech into normal speech without using parallel training data. The most relevant to this task is voice conversion (VC), which converts one speaker’s voice to another. However, it differs in three main respects. First, unlike normal speech, whispered speech contains no or little pitch information. Second, whispered speech usually has significantly less energy than normal speech and is therefore more susceptible to external noise. Third, in the actual usage scenario of W2N-SC, users may suddenly switch voice modes from whispered to normal speech, or vice versa, meaning that the speaking-style of input speech cannot be assumed in advance. To clarify whether existing VC techniques can successfully handle these task-specific concerns and how they should be modified to better address them, we consider a variational autoencoder (VAE)-based VC method as a baseline and examine what modifications to this method would be effective for the current task. Specifically, we study the effects of (1) a self-supervised training scheme called fill-in-frames (FIF), (2) data augmentation (DA) using noisy speech samples, and (3) an architecture that allows for any-to-many conversions. Through experimental evaluation of the W2N-SC and speaker conversion tasks, we confirmed that, especially in the W2N-SC task, the version incorporating the above modifications works better than the baseline VC model applied as is.
AID-purifier: A light auxiliary network for boosting adversarial defense

In this study, we propose AID-purifier that can boost the robustness of adversarially-trained networks by purifying their inputs. AID-purifier is an auxiliary network that works as an add-on to an already trained main classifier. To keep it computationally light, it is trained as a discriminator with a binary cross-entropy loss. To obtain additionally useful information from the adversarial examples, the architecture design is closely related to the information maximization principle where two layers of the main classification network are piped into the auxiliary network. To assist the iterative optimization procedure of purification, the auxiliary network is trained with AVmixup. AID-purifier can be also used together with other purifiers such as PixelDefend for an extra enhancement. Because input purification has been studied relative less when compared to adversarial training or gradient masking, we conduct extensive attack experiments to validate AID-purifier's robustness. The overall results indicate that the best performing adversarially-trained networks can be enhanced further with AID-purifier. The code is available in https://github.com/yelobean/AIDPurifier.
Public sentiment analysis on urban regeneration: A massive data study based on sentiment knowledge enhanced pre-training and latent Dirichlet allocation

Public satisfaction is the ultimate goal and an important determinant of China's urban regeneration plan. This study is the first to use massive data to perform sentiment analysis of public comments on China's urban regeneration.Public comments from social media, online forums, and government affairs platforms are analyzed by a combination of Natural Language Processing, Knowledge Enhanced Pre-Training, Word Cloud, and Latent Dirichlet Allocation.(1) Public sentiment tendency toward China's urban regeneration was generally positive but spatiotemporal divergences were observed; (2) Temporally, public sentiment was most negative in 2020, but most positive in 2021. It has remained consistently negative in 2022, particularly after February 2022; (3) Spatially, at the provincial level, Guangdong posted the most comments and Tibet, Shanghai, Guizhou, Chongqing, and Hong Kong are provinces with highly positive sentiment. At the national level, the east and south coastal, southwestern, and western China regions are more positive, as opposed to the northeast, central, and northwest regions; (4) Topics related to Shenzhen's renovations, development of China's urban regeneration and complaints from residents are validly categorized and become the public's key focus. Accordingly, governments should address spatiotemporal disparities and concerns of local residents for future development of urban regeneration.
Ultra-High Resolution SVBRDF Recovery from a Single Image

Existing convolutional neural networks have achieved great success in recovering Spatially Varying Bidirectional Surface Reflectance Distribution Function (SVBRDF) maps from a single image. However, they mainly focus on handling low-resolution (e.g., 256 × 256) inputs. Ultra-High Resolution (UHR) material maps are notoriously difficult to acquire by existing networks because (1) finite computational resources set bounds for input receptive fields and output resolutions, and (2) convolutional layers operate locally and lack the ability to capture long-range structural dependencies in UHR images. We propose an implicit neural reflectance model and a divide-and-conquer solution to address these two challenges simultaneously. We first crop a UHR image into low-resolution patches, each of which are processed by a local feature extractor to extract important details. To fully exploit long-range spatial dependency and ensure global coherency, we incorporate a global feature extractor and several coordinate-aware feature assembly modules into our pipeline. The global feature extractor contains several lightweight material vision transformers that have a global receptive field at each scale and have the ability to infer long-term relationships in the material. After decoding globally coherent feature maps assembled by coordinate-aware feature assembly modules, the proposed end-to-end method is able to generate UHR SVBRDF maps from a single image with fine spatial details and consistent global structures.
Spectral characterisation of hydrothermal alteration associated with sediment-hosted Cu–Ag mineralisation in the central European Kupferschiefer

Abstract. The analysis of hydrothermal alteration in exploration drill cores allows for fluid–rock interaction processes to be traced, for fluid flow paths to be identified, and thus for vectors in mineral systems to be determined. Hyperspectral imaging techniques are increasingly being employed to fill the scale gap between lab-based petrographic or geochemical analyses and the typical size of exploration targets. Hyperspectral imaging permits the rapid, cost-efficient, and continuous characterisation of alteration mineralogy and texture along entire drill cores, with a spatial sampling of a few millimetres. In this contribution, we present the results of an exploratory study on three mineralised drill cores from the Spremberg–Graustein Kupferschiefer-type Cu–Ag deposit in the Lusatia region of Germany. We demonstrate that hyperspectral imaging is well-suited to recognising and tracking the effects of hydrothermal alteration associated with strata-bound hydrothermal mineralisation. Micro X-ray fluorescence spectrometry was used to corroborate the alteration mineral assemblages identified in hyperspectral data acquired in the visible, near- (400 to 970 nm), shortwave (970 to 2500 nm), mid-wave (2700 to 5300 nm), and longwave infrared (7700 to 12 300 nm). We identified two main shortcomings of the technique, namely the overlapping of some mineral features (e.g. carbonate and illite absorption in the shortwave infrared) and the darkness of the organic-matter-rich dolostones and shales that results in low reflectance. Nevertheless, spectral features associated with iron oxide, kaolinite, sulfate, and carbonates were successfully identified and mapped. We identified different markers of hydrothermal alteration spatially associated with or stratigraphically adjacent to Cu–Ag mineralisation. Importantly, we can clearly distinguish two mineralogically distinct styles of alteration (hematite and ferroan carbonate) that bracket high-grade Cu–Ag mineralisation. Intensive hydrothermal alteration is characterised by the occurrence of well-crystallised kaolinite in the sandstone units immediately below the Kupferschiefer horizon sensu stricto. Proximal Fe-carbonate and kaolinite alteration have not previously been documented for the high-grade Cu–Ag deposits of the central European Kupferschiefer, whereas hematite alteration is well-known in Kupferschiefer-type ore deposits. The latter marks the flow path of oxidising, metal-bearing hydrothermal fluids towards the site of hydrothermal sulfide mineralisation. In contrast, ferroan carbonate alteration in carbonate rocks located above the main mineralised zone is interpreted as a mark of hydrothermal fluid discharge from the mineralising system. Although this study is limited to a small number of drill cores, our results suggest that hyperspectral imaging techniques may be used to identify vectors towards high-grade Cu–Ag mineralisation in Kupferschiefer-type mineral systems.
Parallelized and Cascadable Optical Logic Operations by Few-Layer Diffractive Optical Neural Network

Optical computing has gained much attention due to its high speed, low energy consumption, and the fact that it is naturally parallelizable and multiplexable, etc. Single-bit optical logic gates based on a four-hidden-layer diffractive optical neural network (DONN) have been demonstrated with paired apertures. Here, we show a parallel-logic operation strategy based on two-hidden-layer DONN, showcasing their efficiency by multiple-bit (up to 16-bit) optical logic (e.g., NAND) operations. In addition, we demonstrate how NAND-DONN units can be utilized to achieve NOR and AND operations by flipping and cascading the DONN.