Reviewerly/exHarmony huggingface dataset:
A secure lossless recovery for medical images based on image encoding and data self-embedding

To protect the security of data outsourced to the cloud, the tampers detection and recovery for outsourced image have aroused the concern of people. A secure tampering detection and lossless recovery for medical images (MI) using permutation ordered binary (POB) number system is proposed. In the proposed scheme, the region of interest (ROI) of MI is first extracted, and then, ROI is divided into some no-overlapping blocks, and image encoding is conducted on these blocks based on the better compression performance of JPEG-LS for medical image. After that, the generated compression data by all the blocks are divided into high 4-bit and low 4-bit planes, and shuffling and combination are used to generate two plane images. Owing to the substantial redundancies space in the compressed data, the data of each plane are spread to the size of the original image. Lastly, authentication data of two bits is obtained for every pixel and inserted into the pixel itself within the each plane, and the corresponding 10-bit data is transformed into the POB value of 8-bit. Furthermore, encryption is implemented on the above image to produce two shares which can be outsourced to the cloud server. The users can detect tampered part and recover original image when they down load the shares from the cloud. Extensive experiments on some ordinary medical image and COVID-19 image datasets show that the proposed approach can locate the tampered parts within the MI, and the original MI can be recovered without any loss even if one of the shares are totally destroyed, or two shares are tampered at the ration not more than 50%. Some comparisons and analysis are given to show the better performance of the scheme.
SAT

Adversarial training (AT) has become a popular choice for training robust networks. However, it tends to sacrifice clean accuracy heavily in favor of robustness and suffers from a large generalization error. To address these concerns, we propose Smooth Adversarial Training (SAT), guided by our analysis on the eigenspectrum of the loss Hessian. We find that curriculum learning, a scheme that emphasizes on starting "easy'' and gradually ramping up on the "difficulty'' of training, smooths the adversarial loss landscape for a suitably chosen difficulty metric. We present a general formulation for curriculum learning in the adversarial setting and propose two difficulty metrics based on the maximal Hessian eigenvalue (H-SAT) and the softmax probability (P-SA). We demonstrate that SAT stabilizes network training even for a large perturbation norm and allows the network to operate at a better clean accuracy versus robustness trade-off curve compared to AT. This leads to a significant improvement in both clean accuracy and robustness compared to AT, TRADES, and other baselines. To highlight a few results, our best model improves normal and robust accuracy by 6% and 1% on CIFAR-100 compared to AT, respectively. On Imagenette, a ten-class subset of ImageNet, our model outperforms AT by 23% and 3% on normal and robust accuracy respectively.
A Hybrid Retinex-Based Algorithm for UAV-Taken Image Enhancement

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.
Verifiable Credential Proof Generation and Verification Model for Decentralized SSI-Based Credit Scoring Data

The continuous development of the mobile computing environment has led to the emergence of fintech to enable convenient financial transactions in this environment. Previously proposed financial identity services mostly adopted centralized servers that are prone to single-point-of-failure problems and performance bottlenecks. Blockchain-based self-sovereign identity (SSI), which emerged to address this problem, is a technology that solves centralized problems and allows decentralized identification. However, the verifiable credential (VC), a unit of SSI data transactions, guarantees unlimited right to erasure for self-sovereignty. This does not suit the specificity of the financial transaction network, which requires the restriction of the right to erasure for credit evaluation. This paper proposes a model for VC generation and revocation verification for credit scoring data. The proposed model includes double zero knowledge - succinct non-interactive argument of knowledge (zk-SNARK) proof in the VC generation process between the holder and the issuer. In addition, cross-revocation verification takes place between the holder and the verifier. As a result, the proposed model builds a trust platform among the holder, issuer, and verifier while maintaining the decentralized SSI attributes and focusing on the VC life cycle. The model also improves the way in which credit evaluation data are processed as VCs by granting opt-in and the special right to erasure.
MultiPDF particle filtering in state estimation of nonlinear objects

Abstract This paper presents a new particle filter algorithm (MultiPDF) for state estimation of nonlinear systems. The proposed method is a modification of the standard particle filter approach. Due to the strong need for the acceleration of calculations and an improvement in the estimation quality of state estimation, the authors propose a method which enables one to divide the main particle filter into smaller sub-filters with an accordingly smaller number of particles for each one of them. The algorithm has been implemented for various numbers of particles and subordinate parallel filters. Estimation quality has been checked for nine nonlinear objects (both one- and multidimensional) and evaluated through the quality index, average root-mean-squared error. The computation time of the particle filter algorithm for several hardware configurations has been compared. Based on the obtained results, it can be concluded that, besides the computation acceleration, the parallelization of the particle filter’s operation also improves the estimation quality.
Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.
Faster Homomorphic Encryption over GPGPUs via Hierarchical DGT

Privacy guarantees are still insufficient for outsourced data processing in the cloud. While employing encryption is feasible for data at rest or in transit, it is not for computation without remarkable performance slowdown. Thus, handling data in plaintext during processing is still required, which creates vulnerabilities that can be exploited by malicious entities. Homomorphic encryption schemes enable computation over ciphertexts without knowing the related plaintexts or the decryption key. This work focuses on the challenge of developing an efficient implementation of the BFV scheme on CUDA. This is done by combining and adapting different literature approaches, as the double-CRT representation and the Discrete Galois Transform. Moreover, we propose and implement an improved formulation of the DGT inspired by classical algorithms, which computes the transform up to 2.6 times faster than the state-of-the-art. By using these approaches, we obtain up to 3.6 times faster homomorphic multiplication.
RF-based low-SNR classification of UAVs using convolutional neural networks

Unmanned Aerial Vehicles (UAVs), or drones, which can be considered as a coverage extender for Internet of Everything (IoE), have drawn high attention recently. The proliferation of drones will raise privacy and security concerns in public. This paper investigates the problem of classification of drones from Radio Frequency (RF) fingerprints at the low Signal-to-Noise Ratio (SNR) regime. We use Convolutional Neural Networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.
Sensoria: An exploratory interdisciplinary framework for researching multimodal &amp; sensory experiences

This paper describes the development and salience of an original and innovative interdisciplinary approach, Sensoria, that combines methods and techniques from social science and performance to address the methodological challenges of researching sensory/multimodal experiences. It sets out the core components and methodological principles that underpin the approach and uses an illustrative example to show how it can facilitate research on hard to access sensorial experiences, to access, understand and analyse people’s experiences and perspectives of touch, a highly tacit sensory mode. The paper discusses the methodological contribution and challenges of this approach to sensory research for social science and artistic practice and ‘more-than-representational’ research more generally. It concludes by making a case for more critical research spaces at the intersection of these disciplines to foster multi-dimensional research dialogues and to advance the exploration and understanding of the relationship between the sensory, social and the digital.
Single-Shot Decoding of Linear Rate LDPC Quantum Codes With High Performance

We construct and analyze a family of low-density parity check (LDPC) quantum codes with a linear encoding rate, distance scaling as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n^\epsilon $ </tex-math></inline-formula> for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon &gt; 0$ </tex-math></inline-formula> and efficient decoding schemes. The code family is based on tessellations of closed, four-dimensional, hyperbolic manifolds, as first suggested by Guth and Lubotzky. The main contribution of this work is the construction of suitable manifolds via finite presentations of Coxeter groups, their linear representations over Galois fields and topological coverings. We establish a lower bound on the encoding rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k/n$ </tex-math></inline-formula> of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13/72 = 0.180\ldots $ </tex-math></inline-formula> and we show that the bound is tight for the examples that we construct. Numerical simulations give evidence that parallelizable decoding schemes of low computational complexity suffice to obtain high performance. These decoding schemes can deal with syndrome noise, so that parity check measurements do not have to be repeated to decode. Our data is consistent with a threshold of around 4% in the phenomenological noise model with syndrome noise in the single-shot regime.
Tighter constraints of multiqubit entanglement in terms of unified entropy

We present classes of monogamy inequalities related to the $\alpha$-th ($\alpha \geq 1$) power of the entanglement measure based on the unified-($q,s$) entropy, and polygamy inequalities related to the $\beta$-th ($0 \leq \beta \leq 1$) power of the unified-($q,s$) entanglement of assistance by using Hamming weight. We show that these monogamy and polygamy inequalities are tighter than the existing ones. Detailed examples are given for illustrating the advantages.
The Philosophy of Bitcoin and the Question of Money

Money has been a polarising and unresolved socio-economic issue for more than 300 years. In this article, we explore how the state became increasingly involved in money and, through the words of prominent monetary theorists, identify the problem of the state in money. We analyse Bitcoin to see if it is a solution to this problem but move on to contend that the political dimension needs to be the focus of theory in the 21st century and that control of the supply of money, and the power that it gives, is the root of contention.
Private and Utility Enhanced Recommendations With Local Differential Privacy and Gaussian Mixture Model

Recommendation systems rely heavily on behavioural and preferential data (e.g., ratings and likes) of a user to produce accurate recommendations. However, such unethical data aggregation and analytical practices of Service Providers (SP) causes privacy concerns among users. Local differential privacy (LDP) based perturbation mechanisms address this concern by adding noise to users’ data at the user-side before sending it to the SP. The SP then uses the perturbed data to perform recommendations. Although LDP protects the privacy of users from SP, it causes a substantial decline in recommendation accuracy. We propose an LDP-based Matrix Factorization (MF) with a Gaussian Mixture Model (MoG) to address this problem. The LDP perturbation mechanism, i.e., Bounded Laplace (BLP), regulates the effect of noise by confining the perturbed ratings to a predetermined domain. We derive a sufficient condition of the scale parameter for BLP to satisfy <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -LDP. We use the MoG model at the SP to estimate the noise added locally to the ratings and the MF algorithm to predict missing ratings. Our LDP based recommendation system improves the predictive accuracy without violating LDP principles. We demonstrate that our method offers a substantial increase in recommendation accuracy under a strong privacy guarantee through empirical evaluations on three real-world datasets, i.e., Movielens, Libimseti and Jester.
IST-Unbabel 2021 Submission for the Explainable Quality Estimation Shared Task

We present the joint contribution of Instituto Superior Técnico (IST) and Unbabel to the Explainable Quality Estimation (QE) shared task, where systems were submitted to two tracks: constrained (without word-level supervision) and unconstrained (with word-level supervision).For the constrained track, we experimented with several explainability methods to extract the relevance of input tokens from sentence-level QE models built on top of multilingual pre-trained transformers.Among the different tested methods, composing explanations in the form of attention weights scaled by the norm of value vectors yielded the best results.When word-level labels are used during training, our best results were obtained by using word-level predicted probabilities.We further improve the performance of our methods on the two tracks by ensembling explanation scores extracted from models trained with different pre-trained transformers, achieving strong results for in-domain and zero-shot language pairs.
Partially-Fair Computation from Timed-Release Encryption and Oblivious Transfer

We describe a new protocol to achieve two party \(\varepsilon \)-fair exchange: at any point in the unfolding of the protocol the difference in the probabilities of the parties having acquired the desired term is bounded by a value \(\varepsilon \) that can be made as small as necessary. Our construction uses oblivious transfer and sidesteps previous impossibility results by using a timed-release encryption, that releases its contents only after some lower bounded time. We show that our protocol can be easily generalized to an \(\varepsilon \)-fair two-party protocol for all functionalities. To our knowledge, this is the first protocol to truly achieve \(\varepsilon \)-fairness for all functionalities. All previous constructions achieving some form of fairness for all functionalities (without relying on a trusted third party) had a strong limitation: the fairness guarantee only holds if the honest parties are at least as powerful as the corrupted parties and invest a similar amount of resources in the protocol, an assumption which is often not realistic. Our construction does not have this limitation: our protocol provides a clear upper bound on the running time of all parties, and partial fairness holds even if the corrupted parties have much more time or computational power than the honest parties. Interestingly, this shows that a minimal use of timed-release encryption suffices to circumvent an impossibility result of Katz and Gordon regarding \(\varepsilon \)-fair computation for all functionalities, without having to make the (unrealistic) assumption that the honest parties are as computationally powerful as the corrupted parties – this assumption was previously believed to be unavoidable in order to overcome this impossibility result. We present detailed security proofs of the new construction, which are non-trivial and form the core technical contribution of this work.
Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognition

The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR. In both cases, personalized acoustic models (AM), i.e. fine-tuned AM with specific speaker data, can be built. A question that naturally arises is whether the dissemination of personalized acoustic models can leak personal information. In this paper, we show that it is possible to retrieve the gender of the speaker, but also his identity, by just exploiting the weight matrix changes of a neural acoustic model locally adapted to this speaker. Incidentally we observe phenomena that may be useful towards explainability of deep neural networks in the context of speech processing. Gender can be identified almost surely using only the first layers and speaker verification performs well when using middle-up layers. Our experimental study on the TED-LIUM 3 dataset with HMM/TDNN models shows an accuracy of 95% for gender detection, and an Equal Error Rate of 9.07% for a speaker verification task by only exploiting the weights from personalized models that could be exchanged instead of user data.
Threat Intelligence Sharing Model and Profit Distribution Based on Blockchain and Smart Contracts

The new generation of cyber threat attacks has gradually shifted to APT attacks. The attack technology is complex and changeable. The sharing of threat intelligence can improve network defense capabilities. However, privacy issues, trust issues, and sharing mechanisms make the sharing process inefficient. The characteristics of decentralization, anonymity, and non-tamperability are suitable for solving problems in threat intelligence sharing. In this paper, we propose a new threat intelligence sharing model based on the alliance chain in blockchain technology: CITAShare, which includes a distributed architecture database, and the update of data relies on the consensus algorithm. We build the model to implement the process of sharing threat intelligence through smart contracts and solved the privacy issues in the sharing process consequently. Additionally, we find that current intelligence sharing platforms often lack an effective incentive mechanism, so we propose a profit distribution method based on improved Shapley value to motivate the sharers. Our scheme guaranteed rationality in the operational perspective by using the smart contract in the specific distribution process.
An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification

In natural language understanding, topics that touch upon figurative language and pragmatics are notably difficult.We probe a novel use of locally aggregated descriptors -specifically, an architecture called NeXtVLAD -motivated by its accomplishments in computer vision, achieve tremendous success in the FigLang2020 sarcasm detection task.The reported F 1 score of 93.1% is 14% higher than the next best result.We specifically investigate the extent to which the novel architecture is responsible for this boost, and find that it does not provide statistically significant benefits.Deep learning approaches are expensive, and we hope our insights highlighting the lack of benefits from introducing a resourceintensive component will aid future research to distill the effective elements from long and complex pipelines, thereby providing a boost to the wider research community. TurnTweet Label Context-1The [govt] just confiscated a $180 million boat shipment of cocaine from drug traffickers.Sarcastic Context-2People think 5 tonnes is not a load of cocaine. ResponseMan! I've seen more than that on a Friday night.