Reviewerly/exHarmony huggingface dataset:
Public Adoption of and Trust in the NHS COVID-19 Contact Tracing App in the United Kingdom: Quantitative Online Survey Study

Digital contact tracing is employed to monitor and manage the spread of COVID-19. However, to be effective the system must be adopted by a substantial proportion of the population. Studies of mostly hypothetical contact tracing apps show generally high acceptance, but little is known about the drivers and barriers to adoption of deployed systems.The aim of this study was to investigate adoption of and attitudes toward the NHS (National Health Service) COVID-19 smartphone app, the digital contact tracing solution in the United Kingdom.An online survey based on the extended Technology Acceptance Model with the added factor of trust was carried out with a representative sample of the UK population. Statistical analysis showed adoption rates, attitudes toward and trust in the app, and compliance with self-isolation advice and highlighted differences for vulnerable populations (ie, older adults aged 65 years and over and members of Black, Asian, and minority ethnic [BAME] communities).A total of 1001 participants took part in the study. Around half of the participants who had heard of the NHS COVID-19 mobile phone app (490/963, 50.9%; 95% CI 47.8%-54.0%) had downloaded and kept the app, but more than one-third (345/963, 35.8%; 95% CI 32.8%-38.8%) either did not intend to download it or had deleted it. Significantly more BAME respondents than White respondents had deleted the app (16/115, 13.9%; 95% CI 11.8%-16.0%, vs 65/876, 7.4%; 95% CI 5.8%-9.0%), and significantly more older adults 65 years and over than those under 65 years did not intend to download it (44/127, 34.6%; 95% CI 31.7%-37.5%, vs 220/874, 25.2%; 95% CI 22.5%-27.9%). Broadly, one of the reasons for uptake was to help the NHS and other people, especially among older adults, although significantly fewer BAME participants agreed that they did so to help the NHS. Reported compliance with received notifications to self-isolate was high but was significantly lower than reported intended compliance without received notifications. Only one-fifth (136/699, 19.5%; 95% CI 17.0%-22.0%) of participants understood that the decision to send self-isolation notifications was automated by the app. There were a range of significantly more negative views among BAME participants, including lower trust in the NHS, while older adults were often significantly more positive. Respondents without the app reported significantly lower trust and more negative views toward the app and were less likely to report that they understood how the app works.While compliance on the part of the approximately 50% of participants who had the app was fairly high, there were issues surrounding trust and understanding that hindered adoption and, therefore, the effectiveness of digital contact tracing, particularly among BAME communities. This study highlights that more needs to be done to improve adoption among groups who are more vulnerable to the effects of the virus in order to enhance uptake and acceptance of contact tracing apps.
Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution

Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.
SoK: Modular and Efficient Private Decision Tree Evaluation

Abstract Decision trees and random forests are widely used classifiers in machine learning. Service providers often host classification models in a cloud service and provide an interface for clients to use the model remotely. While the model is sensitive information of the server, the input query and prediction results are sensitive information of the client. This motivates the need for private decision tree evaluation, where the service provider does not learn the client’s input and the client does not learn the model except for its size and the result. In this work, we identify the three phases of private decision tree evaluation protocols: feature selection, comparison, and path evaluation. We systematize constant-round protocols for each of these phases to identify the best available instantiations using the two main paradigms for secure computation: garbling techniques and homomorphic encryption. There is a natural tradeoff between runtime and communication considering these two paradigms: garbling techniques use fast symmetric-key operations but require a large amount of communication, while homomorphic encryption is computationally heavy but requires little communication. Our contributions are as follows: Firstly, we systematically review and analyse state-of-the-art protocols for the three phases of private decision tree evaluation. Our methodology allows us to identify novel combinations of these protocols that provide better tradeoffs than existing protocols. Thereafter, we empirically evaluate all combinations of these protocols by providing communication and runtime measures, and provide recommendations based on the identified concrete tradeoffs.
Cross-Relation Cross-Bag Attention for Distantly-Supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C2SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.
A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized.
Compositional Generalization for Primitive Substitutions

Compositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability.In this paper, we conduct fundamental research for encoding compositionality in neural networks.Conventional methods use a single representation for the input sentence, making it hard to apply prior knowledge of compositionality.In contrast, our approach leverages such knowledge with two representations, one generating attention maps, and the other mapping attended input words to output symbols.We reduce the entropy in each representation to improve generalization.Our experiments demonstrate significant improvements over the conventional methods in five NLP tasks including instruction learning and machine translation.In the SCAN domain, it boosts accuracies from 14.0% to 98.8% in Jump task, and from 92.0% to 99.7% in TurnLeft task.It also beats human performance on a few-shot learning task.We hope the proposed approach can help ease future research towards human-level compositional language learning.Source code is available online 1 .
Single-step implementation of high-fidelity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -bit Toffoli gates

The family of $n$-bit Toffoli gates, with the two-bit Toffoli gate as the figurehead, are of great interest in quantum information as they can be used as universal gates and in quantum error correction, among other things. We present a single-step implementation of arbitrary $n$-bit Toffoli gates (up to a local change of basis), based on resonantly driving a single qubit that has a strong Ising coupling to $n$ other qubits. The setup in the two-qubit case turns out to be identical to the universal Barenco gate. The gate time and error are, in theory, independent of the number of control qubits, scaling better than conventional circuit decompositions. We note that our assumptions, namely strongly coupling $n+1$ qubits and a driving frequency that scales with $n$, may break down for large systems. Still, our protocol could enhance the capabilities of intermediate scale quantum computers, and we discuss the prospects of implementing our protocol on trapped ions, Rydberg atoms, and on superconducting circuits. Simulations of the latter platform show that the Toffoli gate with two control bits attains fidelities of above 0.98 even in the presence of decoherence. We also show how similar ideas can be used to make a series of controlled-\textsc{not}-gates in a single step. We show how these can speed up the implementation of quantum error correcting codes and we simulate the encoding steps of the three-qubit bit-flip code and the seven-qubit Steane code.
Fighting Deepfake by Exposing the Convolutional Traces on Images

Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced users. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. Indeed, discriminating a Deepfake from a real image could be a difficult task even for human eyes but recent works are trying to apply the same technology used for generating images for discriminating them with preliminary good results but with many limitations: employed Convolutional Neural Networks are not so robust, demonstrate to be specific to the context and tend to extract semantics from images. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed. The method is based on the Expectation-Maximization algorithm trained to detect and extract a fingerprint that represents the Convolutional Traces (CT) left by GANs during image generation. The CT demonstrates to have high discriminative power achieving better results than state-of-the-art in the Deepfake detection task also proving to be robust to different attacks. Achieving an overall classification accuracy of over 98%, considering Deepfakes from 10 different GAN architectures not only involved in images of faces, the CT demonstrates to be reliable and without any dependence on image semantic. Finally, tests carried out on Deepfakes generated by FACEAPP achieving 93% of accuracy in the fake detection task, demonstrated the effectiveness of the proposed technique on a real-case scenario.
Targeted Attack for Deep Hashing Based Retrieval

The deep hashing based retrieval method is widely adopted in large-scale image and video retrieval. However, there is little investigation on its security. In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval. Specifically, we first formulate the targeted attack as a point-to-set optimization, which minimizes the average distance between the hash code of an adversarial example and those of a set of objects with the target label. Then we design a novel component-voting scheme to obtain an anchor code as the representative of the set of hash codes of objects with the target label, whose optimality guarantee is also theoretically derived. To balance the performance and perceptibility, we propose to minimize the Hamming distance between the hash code of the adversarial example and the anchor code under the $$\ell ^\infty $$ restriction on the perturbation. Extensive experiments verify that DHTA is effective in attacking both deep hashing based image retrieval and video retrieval.
Information Security Behavior and Information Security Policy Compliance: A Systematic Literature Review for Identifying the Transformation Process from Noncompliance to Compliance

A grave concern to an organization’s information security is employees’ behavior when they do not value information security policy compliance (ISPC). Most ISPC studies evaluate compliance and noncompliance behaviors separately. However, the literature lacks a comprehensive understanding of the factors that transform the employees’ behavior from noncompliance to compliance. Therefore, we conducted a systematic literature review (SLR), highlighting the studies done concerning information security behavior (ISB) towards ISPC in multiple settings: research frameworks, research designs, and research methodologies over the last decade. We found that ISPC research focused more on compliance behaviors than noncompliance behaviors. Value conflicts, security-related stress, and neutralization, among many other factors, provided significant evidence towards noncompliance. At the same time, internal/external and protection motivations proved positively significant towards compliance behaviors. Employees perceive internal and external motivations from their social circle, management behaviors, and organizational culture to adopt security-aware behaviors. Deterrence techniques, management behaviors, culture, and information security awareness play a vital role in transforming employees’ noncompliance into compliance behaviors. This SLR’s motivation is to synthesize the literature on ISPC and ISB, identifying the behavioral transformation process from noncompliance to compliance. This SLR contributes to information system security literature by providing a behavior transformation process model based on the existing ISPC literature.
Can blockchain help food supply chains with platform operations during the COVID-19 outbreak?

Food selling platforms are facing both challenges and opportunities during the COVID-19 outbreak as the enforcement of social distancing protocols has pushed consumers with serious health and safety concerns to shop online. Observing that platforms and their suppliers have adopted blockchain technologies and linked selected information nodes separately to foster consumers’ trust, we establish a game-theoretic model to study the operations decisions and blockchain adoption strategies for a food supply chain consisting of one platform and one supplier. We explore the values and impacts of blockchain on the retailing platform, supplier, and consumers, respectively. An all-win situation is achieved when both members of the supply chain adopt blockchain. We further propose that not all prevalent supply chain contracts can achieve supply chain coordination in the presence of blockchain. In extended studies, we examine the incentives of the supply chain members’ blockchain implementation with consideration of the fixed cost of such adoption, product infection, and tampered information.
A Novel Chaotic Permutation-Substitution Image Encryption Scheme Based on Logistic Map and Random Substitution

Privacy is a serious concern related to sharing videos or images among people over the Internet. As a method to preserve images’ privacy, chaos-based image encryption algorithms have been used widely to fulfil such a requirement. However, these algorithms suffer from a low key-space, significant computational overhead, and a lag in resistance against differential attacks. This paper presents a novel chaos-based image encryption method based on permutation and substitution using a single Substitution Box (S-Box) to address issues in contemporary image encryption algorithms. The proposed encryption technique’s efficiency is validated through extensive experiments as compared to the state-of-the-art encryption algorithms using different measures and benchmarks. Precisely, the collected results demonstrate that the proposed technique is more resilient against well-known statistical attacks and performs well under plaintext attacks. Indeed, the proposed scheme exhibits very high sensitivity concerning the plaintext attack. A minor change in the encryption key or the plain text would result in a completely different encrypted image.
Characterizing Multipartite Entanglement with Moments of Random Correlations

The experimental detection of multipartite entanglement usually requires a number of appropriately chosen local quantum measurements which are aligned with respect to a previously shared common reference frame. The latter, however, can be a challenging prerequisite e.g. for satellite-based photonic quantum communication, making the development of alternative detection strategies desirable. One possibility for avoiding the distribution of classical reference frames is to perform a number of local measurements with settings distributed uniformly at random. In this work we follow such a treatment and show that an improved detection and characterization of multipartite entanglement is possible by combining statistical moments of different order. To do so, we make use of designs which are pseudo-random processes allowing to link the present entanglement criteria to ordinary reference frame independent ones. The strengths of our methods are illustrated in various cases starting with two qubits and followed by more involved multipartite scenarios.
A hybrid approach for named entity recognition in Chinese electronic medical record

With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic. In this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance. The final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours. Our approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.
Impact of digital trends using IoT on banking processes

The internet of things represents the next phase of the digital revolution that will transform the lives of consumers. While the Internet does not usually extend beyond the electronic world, connected objects represent the extension of the Internet to things and places. The purpose of this article is to present the different uses of IoT in finance and to analyze the impact of digital trends and IoT on the procedural scheme of a traditional bank. It would be interesting to gather the different types of digital trends that similarly impact processes of a bank. We will treat these trends in the same way to carry out the digital transformation using these new techniques.