Reviewerly/exHarmony huggingface dataset:
Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets

Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data.We examine selection bias in hate speech in a language and label independent fashion.We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora.We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.
GDPR Compliant Information Confidentiality Preservation in Big Data Processing

Nowadays, new laws and regulations, such as the European General Data Protection Regulation (GDPR), require companies to define privacy policies complying with the preferences of their users.The regulation prescribes expensive penalties for those companies causing the disclosure of sensitive data of their users, even if this occurs accidentally.Thus, it is necessary to devise methods supporting companies in the identification of privacy threats during advanced data manipulation activities.To this end, in this paper, we propose a methodology exploiting relaxed functional dependencies (RFDs) to automatically identify data that could imply the values of sensitive ones, which permits to increase the confidentiality of a dataset while reducing the number of values to be obscured.An experimental evaluation demonstrates the effectiveness of the proposed methodology in increasing compliance to the GDPR data privacy, while reducing the set of values to be partially masked, hence enhancing data usage.
A fast and scalable authentication scheme in IOT for smart living

Numerous resource-limited smart objects (SOs) such as sensors and actuators have been widely deployed in smart environments, opening new attack surfaces to intruders. The severe security flaw discourages the adoption of the Internet of things in smart living. In this paper, we leverage fog computing and microservice to push certificate authority (CA) functions to the proximity of data sources. Through which, we can minimize attack surfaces and authentication latency, and result in a fast and scalable scheme in authenticating a large volume of resource-limited devices. Then, we design lightweight protocols to implement the scheme, where both a high level of security and low computation workloads on SO (no bilinear pairing requirement on the client-side) is accomplished. Evaluations demonstrate the efficiency and effectiveness of our scheme in handling authentication and registration for a large number of nodes, meanwhile protecting them against various threats to smart living. Finally, we showcase the success of computing intelligence movement towards data sources in handling complicated services.
PThammer: Cross-User-Kernel-Boundary Rowhammer through Implicit Accesses

Rowhammer is a hardware vulnerability in DRAM memory, where repeated access to memory can induce bit flips in neighboring memory locations. Being a hardware vulnerability, rowhammer bypasses all of the system memory protection, allowing adversaries to compromise the integrity and confidentiality of data. Rowhammer attacks have shown to enable privilege escalation, sandbox escape, and cryptographic key disclosures.Recently, several proposals suggest exploiting the spatial proximity between the accessed memory location and the location of the bit flip for a defense against rowhammer. These all aim to deny the attacker's permission to access memory locations near sensitive data.In this paper, we question the core assumption underlying these defenses. We present PThammer, a confused-deputy attack that causes accesses to memory locations that the attacker is not allowed to access. Specifically, PThammer exploits the address translation process of modern processors, inducing the processor to generate frequent accesses to protected memory locations. We implement PThammer, demonstrating that it is a viable attack, resulting in a system compromise (e.g., kernel privilege escalation). We further evaluate the effectiveness of proposed software-only defenses showing that PThammer can overcome those.
Construction of domain ontology utilizing formal concept analysis and social media analytics

Semantic Web, deals with the meaning of information in a defined domain and Ontologies are the backbone of Semantic Web. Domain Ontologies are crucial source of information for knowledge-based system. Still, domain ontology development is a labor- intensive process and is highly dependent on developer's knowledge. In this work, a novel semiautomatic method is proposed to build an ontology on terrorism domain. Terrorism activities provide crucial information to enhance security system for any country worldwide. Social media data, namely, Twitter text data is extracted to attain latest information related to domain and next, concepts and relationships are identified and mapped using formal concept analysis. Several user-defined relationships are presented through fluent editor tool. Also, knowledge is extracted with the help of query-based system through a reasoner window of fluent editor. The developed domain ontology is published on web using ontology web language which can be utilized in other related application areas. The proposed work is significant as it develops a wide-coverage domain ontology for terrorism domain using a tool named Fluent Editor, in place of standard tool protégé and semantic information is extracted similar to query-based system with 100% accuracy.
Breaking the trade-off between fast control and long lifetime of a superconducting qubit

Abstract The rapid development in designs and fabrication techniques of superconducting qubits has made coherence times of qubits longer. In the future, however, the radiative decay of a qubit into its control line will be a fundamental limitation, imposing a trade-off between fast control and long lifetime of the qubit. Here, we break this trade-off by strongly coupling another superconducting qubit along the control line. This second qubit, which we call “Josephson quantum filter” (JQF), prevents the first qubit from emitting microwave photons and thus suppresses its relaxation, while transmitting large-amplitude control microwave pulses due to the saturation of the quantum filter, enabling fast qubit control. This device functions as an automatic decoupler between a qubit and its control line and could help in the realization of a large-scale superconducting quantum processor by reducing the heating of the qubit environment and the crosstalk between qubits.
Proposal-Based Video Completion

Video inpainting is an important technique for a wide variety of applications from video content editing to video restoration. Early approaches follow image inpainting paradigms, but are challenged by complex camera motion and non-rigid deformations. To address these challenges flow-guided propagation techniques have been proposed. However, computation of flow is non-trivial for unobserved regions and propagation across a whole video sequence is computationally demanding. In contrast, in this paper, we propose a video inpainting algorithm based on proposals: we use 3D convolutions to obtain an initial inpainting estimate which is subsequently refined by fusing a generated set of proposals. Different from existing approaches for video inpainting, and inspired by well-explored mechanisms for object detection, we argue that proposals provide a rich source of information that permits combining similarly looking patches that may be spatially and temporally far from the region to be inpainted. We validate the effectiveness of our method on the challenging YouTube VOS and DAVIS datasets using different settings and demonstrate results outperforming state-of-the-art on standard metrics.
Quantum Markov Chains Associated with Open Quantum Random Walks

In this paper we construct (nonhomogeneous) quantum Markov chains associated with open quantum random walks. The quantum Markov chain, like the classical Markov chain, is a fundamental tool for the investigation of the basic properties such as reducibility/irreducibility, recurrence/transience, accessibility, ergodicity, etc, of the underlying dynamics. Here we focus on the discussion of the reducibility and irreducibility of open quantum random walks via the corresponding quantum Markov chains. Particularly we show that the concept of reducibility/irreducibility of open quantum random walks in this approach is equivalent to the one previously done by Carbone and Pautrat. We provide with some examples. We will see also that the classical Markov chains can be reconstructed as quantum Markov chains.
Optimal gauge for the multimode Rabi model in circuit QED

Projecting an anharmonic oscillator coupled to a resonator onto a two-level subspace yields a Rabi model. It has been noted that the validity of this truncation is gauge dependent. The authors identify a prescription for finding the optimal gauge in a multiple-resonator scenario.
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images

Accurate Computer-Assisted Diagnosis, associated with proper data wrangling, can alleviate the risk of overlooking the diagnosis in a clinical environment. Towards this, as a Data Augmentation (DA) technique, Generative Adversarial Networks (GANs) can synthesize additional training data to handle the small/fragmented medical imaging datasets collected from various scanners; those images are realistic but completely different from the original ones, filling the data lack in the real image distribution. However, we cannot easily use them to locate disease areas, considering expert physicians' expensive annotation cost. Therefore, this paper proposes Conditional Progressive Growing of GANs (CPGGANs), incorporating highly-rough bounding box conditions incrementally into PGGANs to place brain metastases at desired positions/sizes on 256 × 256 Magnetic Resonance (MR) images, for Convolutional Neural Network-based tumor detection; this first GAN-based medical DA using automatic bounding box annotation improves the training robustness. The results show that CPGGAN-based DA can boost 10% sensitivity in diagnosis with clinically acceptable additional False Positives. Surprisingly, further tumor realism, achieved with additional normal brain MR images for CPGGAN training, does not contribute to detection performance, while even three physicians cannot accurately distinguish them from the real ones in Visual Turing Test.
Blur Removal Via Blurred-Noisy Image Pair

Complex blur such as the mixup of space-variant and space-invariant blur, which is hard to model mathematically, widely exists in real images. In this article, we propose a novel image deblurring method that does not need to estimate blur kernels. We utilize a pair of images that can be easily acquired in low-light situations: (1) a blurred image taken with low shutter speed and low ISO noise; and (2) a noisy image captured with high shutter speed and high ISO noise. Slicing the blurred image into patches, we extend the Gaussian mixture model (GMM) to model the underlying intensity distribution of each patch using the corresponding patches in the noisy image. We compute patch correspondences by analyzing the optical flow between the two images. The Expectation Maximization (EM) algorithm is utilized to estimate the parameters of GMM. To preserve sharp features, we add an additional bilateral term to the objective function in the M-step. We eventually add a detail layer to the deblurred image for refinement. Extensive experiments on both synthetic and real-world data demonstrate that our method outperforms state-of-the-art techniques, in terms of robustness, visual quality, and quantitative metrics.
One-shot entanglement distillation beyond local operations and classical communication

Abstract We study the task of entanglement distillation in the one-shot setting under different classes of quantum operations which extend the set of local operations and classical communication (LOCC). Establishing a general formalism which allows for a straightforward comparison of their exact achievable performance, we relate the fidelity of distillation under these classes of operations with a family of entanglement monotones and the rates of distillation with a class of smoothed entropic quantities based on the hypothesis testing relative entropy. We then characterise exactly the one-shot distillable entanglement of several classes of quantum states and reveal many simplifications in their manipulation. We show in particular that the ε -error one-shot distillable entanglement of any pure state is the same under all sets of operations ranging from one-way LOCC to separability-preserving operations or operations preserving the set of states with positive partial transpose, and can be computed exactly as a quadratically constrained linear program. We establish similar operational equivalences in the distillation of isotropic and maximally correlated states, reducing the computation of the relevant quantities to linear or semidefinite programs. We also show that all considered sets of operations achieve the same performance in environment-assisted entanglement distillation from any state.
RFWash

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAIs). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this paper, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of < 8% when trained on 10-second segments, which reduces manual labelling overhead by ≈ 67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.
Explainable Link Prediction for Emerging Entities in Knowledge Graphs

Despite their large-scale coverage, cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity. Link prediction can alleviate this by inferring a target entity, given a source entity and a query relation. Recent embedding-based approaches operate in an uninterpretable latent semantic vector space of entities and relations, while path-based approaches operate in the symbolic space, making the inference process explainable. However, these approaches typically consider static snapshots of the knowledge graphs, severely restricting their applicability for evolving knowledge graphs with newly emerging entities. To overcome this issue, we propose an inductive representation learning framework that is able to learn representations of previously unseen entities. Our method finds reasoning paths between source and target entities, thereby making the link prediction for unseen entities interpretable and providing support evidence for the inferred link.
Object-Based Illumination Estimation with Rendering-Aware Neural Networks

We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. Conventional inverse rendering is too computationally demanding for real-time applications, and the performance of purely learning-based techniques may be limited by the meager input data available from individual objects. To address these issues, we propose an approach that takes advantage of physical principles from inverse rendering to constrain the solution, while also utilizing neural networks to expedite the more computationally expensive portions of its processing, to increase robustness to noisy input data as well as to improve temporal and spatial stability. This results in a rendering-aware system that estimates the local illumination distribution at an object with high accuracy and in real time. With the estimated lighting, virtual objects can be rendered in AR scenarios with shading that is consistent to the real scene, leading to improved realism.
Extending Attack Graphs to Represent Cyber-Attacks in Communication Protocols and Modern IT Networks

An attack graph is a method used to enumerate the possible paths that an attacker can take in the organizational network. MulVAL is a known open-source framework used to automatically generate attack graphs. MulVAL's default modeling has two main shortcomings. First, it lacks the ability to represent network protocol vulnerabilities, and thus it cannot be used to model common network attacks, such as ARP poisoning. Second, it does not support advanced types of communication, such as wireless and bus communication, and thus it cannot be used to model cyber-attacks on networks that include IoT devices or industrial components. In this article, we present an extended network security model for MulVAL that: (1) considers the physical network topology, (2) supports short-range communication protocols, (3) models vulnerabilities in the design of network protocols, and (4) models specific industrial communication architectures. Using the proposed extensions, we were able to model multiple attack techniques including: spoofing, man-in-the-middle, and denial of service attacks, as well as attacks on advanced types of communication. We demonstrate the proposed model in a testbed which implements a simplified network architecture comprised of both IT and industrial components.
A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction

With the development of industry and technology, the development of the environment and cities has drawn lots of attention. Time series prediction plays a vital role in protecting the environment and improving the level of intelligence and technology in cities, for example prediction of air pollution, water levels, palm oil prices, financial data and grid security. We describe a new algorithm, "Error-output Recurrent Two-layer Extreme Learning Machine" or ERT-ELM: it applied a new recurrent technique, that not only removed the restriction of the prediction horizon problem, but it also used a mean squared error of the current step to update the output weights for the next step. This technique avoided error accumulation in the original recurrent algorithm for multi-step time series prediction. Moreover, the new two-layer structure network improved forecasting compared to conventional single-layer or two-layer ELM models. Quantum behaved Particle Swarm Optimization was used to find suitable ERT-ELM parameters. The ability of our model was assessed on ten data sets—two artificial and eight real-world data sets and performed significantly better than the baselines. Especially for the synthetic data sets, in 1–18 prediction periods, our model achieved mean square errors of 2.64 ×10−3 on the Mackey-Glass data set and 1.49 × 10−4 on the Lorenz data sets.
An Information-Theoretic Perspective on Overfitting and Underfitting

We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbitrary classification algorithm will overfit a dataset. Measuring algorithm capacity via the information transferred from datasets to models, we consider mismatches between algorithm capacities and datasets to provide a signature for when a model can overfit or underfit a dataset. We present results upper-bounding algorithm capacity, establish its relationship to quantities in the algorithmic search framework for machine learning, and relate our work to recent information-theoretic approaches to generalization.
Context Aware Control Systems: An Engineering Applications Perspective

Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data.Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services.This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield.While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations.The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective.Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use.This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society; Food; Health and Well-Being;
Variational optimization of the quantum annealing schedule for the Lechner-Hauke-Zoller scheme

The annealing schedule is optimized for a parameter in the Lechner-Hauke-Zoller (LHZ) scheme for quantum annealing designed for the all-to-all-interacting Ising model representing generic combinatorial optimization problems. We adapt the variational approach proposed by Matsuura et al. (arXiv:2003.09913) to the annealing schedule of a term representing a constraint for variables intrinsic to the LHZ scheme with the annealing schedule of other terms kept intact. Numerical results for a simple ferromagnetic model and the spin-glass problem show that nonmonotonic annealing schedules optimize the performance measured by the residual energy and the final ground-state fidelity. This improvement does not accompany a notable increase in the instantaneous energy gap, which suggests the importance of a dynamical viewpoint in addition to static analyses in the study of practically relevant diabatic processes in quantum annealing.
Natural evolution strategies and variational Monte Carlo

Abstract A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. The recent work of Gomes et al (2019 arXiv:1910.10675) on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies (NES). The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular, it is found that NES can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time.