Reviewerly/exHarmony huggingface dataset:
Are the Russian Banks Threatened with Removal from SWIFT? A Multiple Case Study on Interbank Financial Messaging Systems

When Russia invaded Ukraine in February 2022, the United States, European Union, and their allies removed the Russian banks from the main interbank payment system worldwide, SWIFT (Society for Worldwide Interbank Financial Telecommunication), which is a secure messaging system that makes cross-border payments possible, vital for the international trade. However, it is not the first time that Russian banks are threatened with disconnection from Swift by the US government. In response, the Central Bank of Russia developed in 2014 the SPFS (System for Transfer of Financial Messages, our translation). In addition, the Chinese government created in 2015 an equivalent system named Cross-Border Interbank Payment System (CIPS). In this article, we examined SWIFT and alternatives, such as the Russian SPFS, the Chinese CIPS, and the Indian SFMS. Key findings pointed out crucial differences between the financial messaging systems, their objectives, and the expansion of operations overseas. Finally, discussion and implications to the global economy compile this work.
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing reliable defenses for object detectors against patch attacks is critical but severely understudied. In this paper, we propose Segment and Complete defense (SAC), a general framework for defending object detectors against patch attacks through detection and removal of adversarial patches. We first train a patch segmenter that outputs patch masks which provide pixel-level localization of adversarial patches. We then propose a self adversarial training algorithm to robustify the patch segmenter. In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks. Our experiments on COCO and xView datasets demonstrate that SAC achieves superior robustness even under strong adaptive attacks with no reduction in performance on clean images, and generalizes well to unseen patch shapes, attack budgets, and unseen attack methods. Furthermore, we present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks. Our code is available at https://github.com/joellliu/SegmentAndComplete.
Square Root Unscented Kalman Filter With Modified Measurement for Dynamic State Estimation of Power Systems

Dynamic state estimation of a power system provides essential information about its inherent dynamic change. Nonlinear Kalman filters (NKFs) have been identified as potential versatile tools in performing state estimations. One key challenge of using NKFs lies in the fact that the available observations are inaccurate due to the presence of non-Gaussian noise that degrades the filtering precision and robustness. Some robust NKFs are developed to correct the state estimation via using robust optimality criteria or approximating the posterior density. Considering that the essence in these robust NKFs is to weight the error covariance and noise variance, we propose a novel method that can directly act on measurement to weight error covariance and noise variance. The novel method is based on a square root unscented Kalman filter that can enhance the numerical stability. Then, a weighting factor is applied to the measurement model for alleviating the effect of abnormal measurement errors. The weighting factor is derived from consideration of an anisotropy covariance function and an upper envelope of the traditional &#x201C;sinc&#x201D; function for suppressing large measurement errors and retaining the information of very large errors. We call the new filter the square root unscented Kalman filter with modified measurement (SRUKF-MM). The proposed SRUKF-MM is applied to state estimation of the Western System Coordinating Council (WSCC) 3-machine system and the Northeastern Power Coordinating Council (NPCC) 48-machine system in a non-Gaussian noise environment. Simulation results show that the proposed method achieves significantly improved filtering performance compared to other related NKFs.
Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images

Human activity recognition is one of the most difficult tasks in computer vision.Due to the lack of time information, detecting human activities from still photos is more difficult than sensor-based or videobased techniques.Recently, various deep learning based solutions are being proposed one after another, and their performance is constantly improving.In this paper, we proposed a convolutional neural architecture by ensembling transfer learning based multi-channel attention networks.Here, four CNN branches were used to make feature fusion based ensembling and in each branch, an attention module was used to extract the contextual information from the feature map produced by existing pre-trained models.Finally, the extracted feature maps from four branches were concatenated and fed to fully connected network to produce the final recognition output.We considered 3 different datasets, Stanford 40 actions, BU-101 and Willow human actions datasets to evaluate our system.Experimental analysis showed that the proposed ensembled convolutional architecture outperformed previous works by a noteworthy margin.
Unsupervised Histological Image Registration Using Structural Feature Guided Convolutional Neural Network

Registration of multiple stained images is a fundamental task in histological image analysis. In supervised methods, obtaining ground-truth data with known correspondences is laborious and time-consuming. Thus, unsupervised methods are expected. Unsupervised methods ease the burden of manual annotation but often at the cost of inferior results. In addition, registration of histological images suffers from appearance variance due to multiple staining, repetitive texture, and section missing during making tissue sections. To deal with these challenges, we propose an unsupervised structural feature guided convolutional neural network (SFG). Structural features are robust to multiple staining. The combination of low-resolution rough structural features and high-resolution fine structural features can overcome repetitive texture and section missing, respectively. SFG consists of two components of structural consistency constraints according to the formations of structural features, i.e., dense structural component and sparse structural component. The dense structural component uses structural feature maps of the whole image as structural consistency constraints, which represent local contextual information. The sparse structural component utilizes the distance of automatically obtained matched key points as structural consistency constraints because the matched key points in an image pair emphasize the matching of significant structures, which imply global information. In addition, a multi-scale strategy is used in both dense and sparse structural components to make full use of the structural information at low resolution and high resolution to overcome repetitive texture and section missing. The proposed method was evaluated on a public histological dataset (ANHIR) and ranked first as of Jan 18th, 2022.
Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs

Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance in many aerial vision-based applications. Despite the great success of generic object detection methods, a significant performance drop is observed when applied to images captured by UAVs. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and different capture times. These variations lead to domain imbalances and, thus, trained models suffering from domain bias. We demonstrate that domain knowledge is a valuable source of information and thus propose domain-aware object detectors by using freely accessible sensor data. By splitting the model into cross-domain and domain-specific parts, substantial performance improvements are achieved on multiple data sets across various models and metrics without changing the architecture. In particular, we achieve a new state-of-the-art performance on UAVDT for embedded real-time detectors. Furthermore, we create a new airborne image data set by annotating 13,713 objects in 2,900 images featuring precise altitude and viewing angle annotations.
Cryptoanalysis on a Cloud-Centric Internet-of-Medical-Things-Enabled Smart Healthcare System

The interconnecting of the biomedical sensors (in healthcare system) with cloud for the internet-of-medical-things (IoMT) technology has great potential to ameliorate people's living conditions.The privacy-preserving of personal health information (PHI) and the mutual authentication between the sensors and other entities are two main factors that affect the further applications of cloud-centric IoMT technology.In the recent work [IEEE IoT Journal, vol.7(10), 10650-10659, 2020], Kumar and Chand applied identity-based aggregate signcryption scheme to the smart healthcare system (KC-system, for short), which provides privacy-preserving of PHI and the mutual authentication function, simultaneously.However, in this paper, we carefully analyze the security of KC-system and find out that the critical authentication keys of entities can be easily recovered from their communication contents.In other words, the mutual authentication function of KC-system can be easily broken.Moreover, the recovering of the keys will lead to the tedious processes, including obtaining partial private key (from network manager) and requesting for key-protection (from key-protection servers), become completely useless.Finally, we also twist their protocol into a new one, which can be proven secure against the previous attack.
Reflection algebras and conservation results for theories of iterated truth

We consider extensions of the language of Peano arithmetic by transfinitely iterated truth definitions satisfying uniform Tarskian biconditionals. Without further axioms, such theories are known to be conservative extensions of the original system of arithmetic. Much stronger systems, however, are obtained by adding either induction axioms or reflection axioms on top of them. Theories of this kind can interpret some well-known predicatively reducible fragments of second-order arithmetic such as iterated arithmetical comprehension. We obtain sharp results on the proof-theoretic strength of these systems using methods of provability logic. Reflection principles naturally define unary operators acting on the semilattice of axiomatizable extensions of our basic theory of iterated truth. The substructure generated by the top element of this algebra provides a canonical ordinal notation system for the class of theories under investigation. Using these notations we obtain conservativity relationships for iterated reflection principles of different logical complexity levels corresponding to the levels of the hyperarithmetical hierarchy, i.e., the analogs of Schmerl's formulas. These relationships, in turn, provide proof-theoretic analysis of our systems and of some related predicatively reducible theories. In particular, we uniformly calculate the ordinals characterizing the standard measures of their proof-theoretic strength, such as provable well-orderings, classes of provably recursive functions, and Π10-ordinals.
Quantum Speedup for Inferring the Value of Each Bit of a Solution State in Unsorted Databases Using a Bio-Molecular Algorithm on IBM Quantum’s Computers

In this paper, we propose a bio-molecular algorithm with O( n 2) biological operations, O( 2n-1 ) DNA strands, O( n ) tubes and the longest DNA strand, O( n ), for inferring the value of a bit from the only output satisfying any given condition in an unsorted database with 2n items of n bits. We show that the value of each bit of the outcome is determined by executing our bio-molecular algorithm n times. Then, we show how to view a bio-molecular solution space with 2n-1 DNA strands as an eigenvector and how to find the corresponding unitary operator and eigenvalues for inferring the value of a bit in the output. We also show that using an extension of the quantum phase estimation and quantum counting algorithms computes its unitary operator and eigenvalues from bio-molecular solution space with 2n-1 DNA strands. Next, we demonstrate that the value of each bit of the output solution can be determined by executing the proposed extended quantum algorithms n times. To verify our theorem, we find the maximum-sized clique to a graph with two vertices and one edge and the solution b that satisfies b2 ≡ 1 (mod 15) and using IBM Quantum's backend.
High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics

Abstract In Neural Machine Translation, it is typically assumed that the sentence with the highest estimated probability should also be the translation with the highest quality as measured by humans. In this work, we question this assumption and show that model estimates and translation quality only vaguely correlate. We apply Minimum Bayes Risk (MBR) decoding on unbiased samples to optimize diverse automated metrics of translation quality as an alternative inference strategy to beam search. Instead of targeting the hypotheses with the highest model probability, MBR decoding extracts the hypotheses with the highest estimated quality. Our experiments show that the combination of a neural translation model with a neural reference-based metric, Bleurt, results in significant improvement in human evaluations. This improvement is obtained with translations different from classical beam-search output: These translations have much lower model likelihood and are less favored by surface metrics like Bleu.
Content matters: Measures of contextual diversity must consider semantic content

Measures of contextual diversity seek to replace word frequency by counting the number of different contexts that a word occurs in rather than the total raw number of occurrences (Adelman, Brown, & Quesada, 2006). It has repeatedly been shown that contextual diversity measures outperform word frequency on word recognition datasets (Adelman & Brown, 2008; Brysbaert & New, 2009). Recently, Hollis (2020) demonstrated that the standard operationalization of contextual diversity as a document count accounts for relatively little unique variance over word frequency when other variables of contextual occurrences are controlled for. One aspect of the analysis conducted by Hollis (2020) that was not taken into account was the semantic content of the contexts that words occur in. Johns, Dye, and Jones (2020) and Johns (2021) have recently shown that defining linguistic contexts at larger, and more ecologically valid, levels lead to contextual diversity measures that provide very large improvements over word frequency, especially when implemented with principles from the Semantic Distinctiveness Model of Jones, Johns, and Recchia (2012). Across a series of simulations, we demonstrate that the advantages of contextual diversity measures are dependent upon the usage of semantic representations of words to determine the uniqueness of contextual occurrences, where unique contextual occurrences provide a greater impact to a word's lexical strength than redundant contextual occurrences. The results of the simulations suggest that for better theoretical accounts of lexical strength to be developed, attention needs to be paid to the representation of linguistic contexts. Code and data associated with this article is available at https://osf.io/r5ec2/.
Efficient Verifiably Encrypted ECDSA-Like Signatures and Their Applications

Verifiably encrypted signature (VES) allows a signer to encrypt a signature under the public key of a trusted third party (aka adjudicator) in a verifiable manner. Recently, Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a practical verifiably encrypted signature scheme for ECDSA and initiated the study of escrow protocol for Bitcoin via VES. This paper generalizes and improves the VES scheme of Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , such that it covers a family of signatures with similar structures, including ECDSA, Schnorr and their variants. Our construction is very efficient: comparing with Yang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> ’s construction, the size of the resulting VES (for ECDSA) is reduced by more than 25 times. The only caveat is that the adjudicator is required to store a look-up table of size around 270MB. Our scheme naturally gives rise to escrow protocols for mainstream cryptocurrencies that employ ECDSA-like signatures to authorise transaction, including Bitcoin, Ethereum, Cardano, Chainlink, etc.
Multi-Task Language Modeling for Improving Speech Recognition of Rare Words

End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the average accuracy of these systems may be high, the performance on rare content words often lags behind hybrid ASR systems. To address this problem, second-pass rescoring is often applied leveraging upon language modeling (LM). In this paper, we propose a second-pass system with multi-task learning, utilizing semantic targets (such as intent and slot prediction) to improve speech recognition performance. We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the LM task, by 1.4% on a general test and by 2.6% on a rare word test set in terms of word-error-rate relative (WERR). Our best ASR system with multi-task LM shows 4.6% WERR deduction compared with RNN Transducer only ASR baseline for rare words recognition.
Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects

Although interaction effects can be exploited to improve predictions and allow for valuable insights into covariate interplay, they are given limited attention in analysis. Interaction forests are a variant of random forests for categorical, continuous, and survival outcomes that explicitly models quantitative and qualitative interaction effects in bivariable splits performed by the trees constituting the forests. The new effect importance measure (EIM) associated with interaction forests allows for ranking of covariate pairs with respect to their interaction effects' importance to prediction. Using EIM, separate importance value lists for univariable effects, quantitative interaction effects, and qualitative interaction effects are obtained. In the spirit of interpretable machine learning, the bivariable split types of interaction forests target easily interpretable and communicable interaction effects. To learn about the nature of the interplay between covariates identified as interacting it is convenient to visualise their estimated bivariable influence. Functions that perform this task are provided in the R package diversityForest, which implements interaction forests. In a large-scale empirical study using 220 data sets, interaction forests tended to deliver better predictions than conventional random forests and competing random forest variants that use multivariable splitting. In a simulation study, EIM delivered considerably better rankings for the relevant quantitative and qualitative interaction effects than competing approaches. These results indicate that interaction forests are suitable tools for the challenging task of identifying and making use of easily interpretable and communicable interaction effects in predictive modelling.
Multidimensional Deformable Object Manipulation Based on DN-Transporter Networks

In the process of transportation, the handling and loading methods of rigid objects are becoming more and more perfect. However, whether in today’s transportation system or in daily life, such as packing objects or sorting cables before transportation, the manipulation of deformable objects has been always inevitable and has attracted more and more attention. Due to the super degrees of freedom and the unpredictable physical state of deformed objects. It is difficult for robots to complete tasks under the environment of the deformable object. Therefore, we present a method based on imitation learning. In the generated expert demonstration, the agent is offered to learn the state sequence, and then imitate the expert’s trajectory sequence which avoid the above-mentioned difficulties. In addition, compared with the baseline method, our proposed DN-Transporter Networks are more competitive in a simulation environment involving cloth, ropes or bags.
QLP: Deep Q-Learning for Pruning Deep Neural Networks

We present a novel, deep Q-learning based method, QLP, for pruning deep neural networks (DNNs). Given a DNN, our method intelligently determines favorable layer-wise sparsity ratios, which are then implemented via unstructured, magnitude-based, weight pruning. In contrast to previous reinforcement learning (RL) based pruning methods, our method is not forced to prune a DNN within a single, sequential pass from the first layer to the last. It visits each layer multiple times and prunes them little by little at each visit, achieving superior granular pruning. Moreover, our method is not restricted to a subset of actions within the feasible action space. It has the flexibility to execute a whole range of sparsity ratios (0% - 100%) for each layer. This enables aggressive pruning without compromising accuracy. Furthermore, our method does not require a complex state definition; it features a simple, generic definition that is composed of only the index and the density of the layers, which leads to less computational demand while observing the state at each interaction. Lastly, our method utilizes a carefully designed curriculum that enables learning targeted policies for each sparsity regime, which helps to deliver better accuracy, especially at high sparsity levels. We conduct batched performance tests at compelling sparsity levels (up to 98%), present extensive ablation studies to justify our RL-related design choices, and compare our method with the state-of-the-art, including RL-based and other pruning methods. Our method sets the new state-of-the-art results in most of the experiments with ResNet-32 and ResNet-56 over CIFAR-10 dataset as well as ResNet-50 and MobileNet-v1 over ILSVRC2012 (ImageNet) dataset.