Reviewerly/exHarmony huggingface dataset:
Testing Robustness Against Unforeseen Adversaries

Adversarial robustness research primarily focuses on L_p perturbations, and most defenses are developed with identical training-time and test-time adversaries. However, in real-world applications developers are unlikely to have access to the full range of attacks or corruptions their system will face. Furthermore, worst-case inputs are likely to be diverse and need not be constrained to the L_p ball. To narrow in on this discrepancy between research and reality we introduce ImageNet-UA, a framework for evaluating model robustness against a range of unforeseen adversaries, including eighteen new non-L_p attacks. To perform well on ImageNet-UA, defenses must overcome a generalization gap and be robust to a diverse attacks not encountered during training. In extensive experiments, we find that existing robustness measures do not capture unforeseen robustness, that standard robustness techniques are beat by alternative training strategies, and that novel methods can improve unforeseen robustness. We present ImageNet-UA as a useful tool for the community for improving the worst-case behavior of machine learning systems.
Temporal Pattern Recognition with Delayed-Feedback Spin-Torque Nano-Oscillators

The recent demonstration of neuromorphic computing with spin-torque nano-oscillators points to substantial energy-saving in data analysis. However, the limited intrinsic memory of these devices (much less than a microsecond) limits their utility for analyzing temporal sequences. Here the authors overcome the short memory of a spin-torque oscillator by using a feedback loop with a delay of 1 $\ensuremath{\mu}$s, reducing the error rate during temporal-pattern classification by 99%. In addition, they determine optimal operating point of the oscillator, in terms of the current and magnetic field, to take advantage of this memory for recognition tasks.
LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19, MobileNet, and ShuffleNet to provide multiple trade-offs between accuracy and computational cost. The proposed model LiteSeg, with MobileNetV2 as a backbone network, achieves an accuracy of 67.81% mean intersection over union at 161 frames per second with 640 × 360 resolution on the Cityscapes dataset.
Studying the accumulation velocity of altmetric data tracked by Altmetric.com

Abstract This paper investigates the data accumulation velocity of 12 Altmetric.com data sources. DOI created date recorded by Crossref and altmetric event posted date tracked by Altmetric.com are combined to reflect the altmetric data accumulation patterns over time and to compare the data accumulation velocity of various data sources through three proposed indicators, including Velocity Index , altmetric half - life , and altmetric time delay . Results show that altmetric data sources exhibit different data accumulation velocity. Some altmetric data sources have data accumulated very fast within the first few days after publication, such as Reddit, Twitter, News, Facebook, Google+, and Blogs. On the opposite spectrum, research outputs are at relatively slow pace in accruing data on some data sources, like Policy documents, Peer review, Q&amp;A, Wikipedia, Video, and F1000Prime. Most altmetric data sources’ velocity degree also changes by document types, subject fields, and research topics. The type Review is slower in receiving altmetric mentions than Article, while Editorial Material and Letter are typically faster. In general, most altmetric data sources show higher velocity values in the fields of Physical Sciences and Engineering and Life and Earth Sciences . Within each field, there also exist some research topics that attract social attention faster than others.
Hardware efficient quantum algorithms for vibrational structure calculations

We introduce a framework for the calculation of ground and excited state energies of bosonic systems suitable for near-term quantum devices and apply it to molecular vibrational anharmonic Hamiltonians. Our method supports generic reference modal bases and Hamiltonian representations, including the ones that are routinely used in classical vibrational structure calculations. We test different parametrizations of the vibrational wavefunction, which can be encoded in quantum hardware, based either on heuristic circuits or on the bosonic Unitary Coupled Cluster Ansatz. In particular, we define a novel compact heuristic circuit and demonstrate that it provides a good compromise in terms of circuit depth, optimization costs, and accuracy. We evaluate the requirements, number of qubits and circuit depth, for the calculation of vibrational energies on quantum hardware and compare them with state-of-the-art classical vibrational structure algorithms for molecules with up to seven atoms.
Collision Models Can Efficiently Simulate Any Multipartite Markovian Quantum Dynamics

We introduce the multipartite collision model, defined in terms of elementary interactions between subsystems and ancillae, and show that it can simulate the Markovian dynamics of any multipartite open quantum system. We develop a method to estimate an analytical error bound for any repeated interactions model, and we use it to prove that the error of our scheme displays an optimal scaling. Finally, we provide a simple decomposition of the multipartite collision model into elementary quantum gates, and show that it is efficiently simulable on a quantum computer according to the dissipative quantum Church-Turing theorem, i.e. it requires a polynomial number of resources.
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning

Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag’s label is known, the main challenge is assigning which key instances within the bag to trigger the bag’s label. Most previous models use attention-based approaches applying attentions to generate the bag’s representation from instances, and then train it via the bag’s classification. These models, however, implicitly violate the MIL assumption that instances in negative bags should be uniformly negative. In this work, we explicitly model the key instances assignment as a hidden variable and adopt an Expectation-Maximization (EM) framework. We derive two pseudo-label generation schemes to model the E and M process and iteratively optimize the likelihood lower bound. We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions. It achieves state-of-the-art performance on two standard benchmarks, THUMOS14 and ActivityNet1.2.
Privacy-aware eye tracking using differential privacy

With eye tracking being increasingly integrated into virtual and augmented reality (VR/AR) head-mounted displays, preserving users' privacy is an ever more important, yet under-explored, topic in the eye tracking community. We report a large-scale online survey (N=124) on privacy aspects of eye tracking that provides the first comprehensive account of with whom, for which services, and to what extent users are willing to share their gaze data. Using these insights, we design a privacy-aware VR interface that uses differential privacy, which we evaluate on a new 20-participant dataset for two privacy sensitive tasks: We show that our method can prevent user re-identification and protect gender information while maintaining high performance for gaze-based document type classification. Our results highlight the privacy challenges particular to gaze data and demonstrate that differential privacy is a potential means to address them. Thus, this paper lays important foundations for future research on privacy-aware gaze interfaces.
Optimal stochastic extragradient schemes for pseudomonotone stochastic variational inequality problems and their variants

We consider the stochastic variational inequality problem in which the map is expectation-valued in a component-wise sense. Much of the available convergence theory and rate statements for stochastic approximation schemes are limited to monotone maps. However, non-monotone stochastic variational inequality problems are not uncommon and are seen to arise from product pricing, fractional optimization problems, and subclasses of economic equilibrium problems. Motivated by the need to address a broader class of maps, we make the following contributions: (1) we present an extragradient-based stochastic approximation scheme and prove that the iterates converge to a solution of the original problem under either pseudomonotonicity requirements or a suitably defined acute angle condition. Such statements are shown to be generalizable to the stochastic mirror-prox framework; (2) under strong pseudomonotonicity, we show that the mean-squared error in the solution iterates produced by the extragradient SA scheme converges at the optimal rate of $${{\mathcal {O}}}\left( \frac{1}{{K}}\right) $$, statements that were hitherto unavailable in this regime. Notably, we optimize the initial steplength by obtaining an $$\epsilon $$-infimum of a discontinuous nonconvex function. Similar statements are derived for mirror-prox generalizations and can accommodate monotone SVIs under a weak-sharpness requirement. Finally, both the asymptotics and the empirical rates of the schemes are studied on a set of variational problems where it is seen that the theoretically specified initial steplength leads to significant performance benefits.
Multi-Objective CNN-Based Algorithm for SAR Despeckling

Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous.
SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Internet of Things (IoT) is a domain where the transfer of big data is taking place every single second. The security of these data is a challenging task; however, security challenges can be mitigated with cryptography and steganography techniques. These techniques are crucial when dealing with user authentication and data privacy. In the proposed work, a highly secured technique is proposed using IoT protocol and steganography. This work proposes an image steganography procedure by utilizing the combination of various algorithms that build the security of the secret data by utilizing Binary bit-plane decomposition (BBPD) based image encryption technique. Thereafter a Salp Swarm Optimization Algorithm (SSOA) based adaptive embedding process is proposed to increase the payload capacity by setting different parameters in the steganographic embedding function for edge and smooth blocks. Here the SSOA algorithm is used to localize the edge and smooth blocks efficiently. Then, the hybrid Fuzzy Neural Network with a backpropagation learning algorithm is used to enhance the quality of the stego images. Then these stego images are transferred to the destination in the highly secured protocol of IoT. The proposed steganography technique shows better results in terms of security, image quality, and payload capacity in comparison with the existing state of art methods.
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Product reviews contain a large number of implicit aspects and opinions.However, most of the existing studies in aspect-based sentiment analysis ignored this problem.In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions.We further construct two new datasets Restaurant-ACOS and Laptop-ACOS for this new task.The former is an extension of the SemEval Restaurant dataset; the latter is a brand new Laptop dataset with much larger size than the Se-mEval Laptop dataset.Both contain the annotations of not only aspect-category-opinionsentiment quadruples but also implicit aspects and opinions.We finally benchmark the task with four baseline systems.Experiments demonstrate the feasibility of the new task and its advantage in extracting and describing implicit aspects and implicit opinions in ABSA.The two datasets and source code of four systems are publicly released at https: //github.com/NUSTM/ACOS.
Developing a cyber security culture: Current practices and future needs

While the creation of a strong security culture has been researched and discussed for decades, it continues to elude many businesses. Part of the challenge faced is distilling pertinent, recent academic findings and research into useful guidance. In this article, we aim to tackle this issue by conducting a state-of-the-art study into organisational cyber security culture research. This work investigates four questions, including how cyber security culture is defined, what factors are essential to building and maintaining such a culture, the frameworks proposed to cultivate a security culture and the metrics suggested to assess it. Through the application of the PRISMA systematic literature review technique, we identify and analyse 58 research articles from the last 10 years (2010-2020). Our findings demonstrate that while there have been notable changes in the use of terms (e.g., information security culture and cyber security culture), many of the most influential factors across papers are similar. Top management support, policy and procedures, and awareness for instance, are critical in engendering cyber security culture. Many of the frameworks reviewed revealed common foundations, with organisational culture playing a substantial role in crafting appropriate cyber security culture models. Questionnaires and surveys are the most used tool to measure cyber security culture, but there are also concerns as to whether more dynamic measures are needed. For practitioners, this article highlights factors and models essential to the creation and management of a robust security culture. For research, we produce an up-to-date characterisation of the field and also define open issues deserving of further attention such as the role of change management processes and national culture in an enterprise's cyber security culture.
Homodyne Detection Quadrature Phase Shift Keying Continuous-Variable Quantum key Distribution with High Excess Noise Tolerance

Discrete-modulated continuous-variable quantum key distribution with homodyne detection is widely recognized for its ease of implementation, efficiency with respect to error correction, and its compatibility with modern optical communication devices. However, recent studies report that the application of homodyne detection obtains poor tolerance to excess noise and insufficient transmission distance, hence seriously restricting the large-scale deployment of quantum secure communication networks. In this paper, we propose a homodyne detection protocol using the quadrature phase shift keying technique. By limiting information leakage, our proposed protocol enhances excess noise tolerance to a high level. Furthermore, we demonstrate that homodyne detection performs better than heterodyne detection in quaternary-modulated continuous-variable quantum key distribution under the untrusted detector noise scenario. The security is analyzed using the tight numerical method against collective attacks in the asymptotic regime. Our results imply that the current protocol can distribute keys in nearly intercity area and, thus, paves the way for constructing low-cost quantum secure communication networks.3 MoreReceived 2 May 2021Revised 15 July 2021Accepted 21 October 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040334Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum networksQuantum opticsQuantum InformationAtomic, Molecular & Optical
Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.
Blockchain Technology and Smart Contracts in Decentralized Governance Systems

The aim of our systematic review was to inspect the recently published literature on decentralized governance systems and integrate the insights it articulates on blockchain technology and smart contracts by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout January and May 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was carried out, with search terms including “city” + “blockchain technology”, “smart contracts”, and “decentralized governance systems”. As the analyzed research studies were published between 2016 and 2022, only 371 sources satisfied the eligibility criteria. A Shiny app was harnessed for the PRISMA flow diagram to include evidence-based acquired and handled data. Analyzing the most recent and relevant sources and leveraging screening and quality assessment tools such as AMSTAR, Dedoose, Distiller SR, ROBIS, and SRDR, we integrated the core outcomes and robust correlations related to smart urban governance. As data visualization tools, for initial bibliometric mapping dimensions were harnessed, together with layout algorithms provided by VOSviewer. Future research should investigate smart contract governance of blockchain applications and infrastructure using decision-making tools and spatial cognition algorithms.
Geo-Supervised Visual Depth Prediction

We propose using global orientation from inertial measurements, and the bias it induces on the shape of objects populating the scene, to inform visual three-dimensional reconstruction. We test the effect of using the resulting prior in-depth prediction from a single image, where the normal vectors to surfaces of objects of certain classes tend to align with gravity or be orthogonal to it. Adding such a prior to baseline methods for monocular depth prediction yields improvements beyond the state-of-the-art and illustrates the power of gravity as a supervisory signal.
Quantum simulation of scattering in the quantum Ising model

We discuss real time evolution for the quantum Ising model in one spatial dimension with $N_s$ sites. In the limit where the nearest neighbor interactions $J$ in the spatial directions are small, there is a simple physical picture where qubit states can be interpreted as approximate particle occupations. Using exact diagonalization, for initial states with one or two particles, we show that for small $J$, discrete Bessel functions provide very accurate expressions for the evolution of the occupancies corresponding to initial states with one and two particles. Boundary conditions play an important role when the evolution time is long enough. We discuss a Trotter procedure to implement the evolution on existing quantum computers and discuss the error associated with the Trotter step size. We discuss the effects of gate and measurement errors on the evolution of one and two particle states using 4 and 8 qubits circuits approximately corresponding to existing or near term quantum computers.