Reviewerly/exHarmony huggingface dataset:
The meaning of Freedom after Covid-19

Many governments have seen digital health technologies as promising tools to tackle the current COVID-19 pandemic. A much-talked example in this context involves the recent deluge of digital contact tracing apps (DCT) aimed at detecting Covid-19 exposure. In this short contribution we look at the bio-political justification of this phenomenon and reflect on whether DCT apps constitute, as it is often argued, a serious potential breach of our right to privacy. Despite praising efforts attempting to develop legal and ethical frameworks for DCT apps' usage; we argue that such endeavours are not sufficient to tackle the more fundamental problem of mass surveillance, which will remain largely unaddressed unless we deal with the biopolitical arguments presented and resort to a technical and structural defence.
Finding Small and Large $k$-Clique Instances on a Quantum Computer

Algorithms for triangle-finding, the smallest nontrivial instance of the k-clique problem, have been proposed for quantum computers. Still, those algorithms assume the use of fixed access time quantum RAM (QRAM). We present a practical gate-based approach to both the triangle-finding problem and its NP-hard k-clique generalization. We examine both constant factors for near-term implementation on a Noisy Intermediate Scale Quantum computer (NISQ) device, and the scaling of the problem to evaluate long-term use of quantum computers. We compare the time complexity and circuit practicality of the theoretical approach and actual implementation. We propose and apply two different strategies to the k-clique problem, examining the circuit size of Qiskit implementations. We analyze our implementations by simulating triangle finding with various error models, observing the effect on damping the amplitude of the correct answer, and compare to execution on six real IBMQ machines. Finally, we estimate the date when the methods proposed can run effectively on an actual device based on IBM's quantum volume exponential growth forecast and the results of our error analysis.
Quantum randomness protected against detection loophole attacks

Device and semi-device-independent private quantum randomness generators are crucial for applications requiring private randomness. However, they are vulnerable to detection inefficiency attacks and this limits severely their usage for practical purposes. Here, we present a method for protecting semi-device-independent private quantum randomness generators in prepare-and-measure scenarios against detection inefficiency attacks. The key idea is the introduction of a blocking device that adds failures in the communication between the preparation and measurement devices. We prove that, for any detection efficiency, there is a blocking rate that provides protection against these attacks. We experimentally demonstrate the generation of private randomness using weak coherent states and standard avalanche photo-detectors.
Obstacle Avoidance via Hybrid Feedback

In this paper we present a hybrid feedback approach to solve the navigation problem of a point mass in the n-dimensional space containing an arbitrary number of ellipsoidal shape obstacles. The proposed hybrid control algorithm guarantees both global asymptotic stabilization to a reference and avoidance of the obstacles. The intuitive idea of the proposed hybrid feedback is to switch between two modes of control: stabilization and avoidance. The geometric construction of the flow and jump sets of the proposed hybrid controller, exploiting hysteresis regions, guarantees Zeno-free switching between the stabilization and the avoidance modes. Simulation results illustrate the performance of the proposed hybrid control approach for 2-dimensional and 3-dimensional scenarios.
Classical Simulation and Theory of Quantum Annealing in a Thermal Environment

We study quantum annealing in the quantum Ising model coupled to a thermal environment. When the speed of quantum annealing is sufficiently slow, the system evolves following the instantaneous thermal equilibrium. This quasistatic and isothermal evolution, however, fails near the end of annealing because the relaxation time grows infinitely, therefore yielding excess energy from the thermal equilibrium. We develop a phenomenological theory based on this picture and derive a scaling relation of the excess energy after annealing. The theoretical results are numerically confirmed using a novel non-Markovian method that we recently proposed based on a path-integral representation of the reduced density matrix and the infinite time evolving block decimation. In addition, we discuss crossovers from weak to strong coupling as well as from the adiabatic to quasistatic regime, and propose experiments on the D-Wave quantum annealer.
Frequency Selective Auto-Encoder for Smart Meter Data Compression

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process.
Simulating non-native cubic interactions on noisy quantum machines

As a milestone for general-purpose computing machines, we demonstrate that quantum processors can be programed to efficiently simulate dynamics that are not native to the hardware. Moreover, on noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates instead of a restricted set of standard gates. We demonstrate the general methodology by solving a cubic interaction problem, which appears in nonlinear optics, gauge theories, as well as plasma and fluid dynamics. To encode the non-native Hamiltonian evolution, we decompose the Hilbert space into a direct sum of invariant subspaces in which the nonlinear problem is mapped to a finite-dimensional Hamiltonian simulation problem. In a three-states example, the resultant unitary evolution is realized by a product of approximately 20 standard gates, using which approximately ten simulation steps can be carried out on state-of-the-art quantum hardware before results are corrupted by decoherence. In comparison, the simulation depth is improved by more than an order of magnitude when the unitary evolution is realized as a single cubic gate, which is compiled directly using optimal control. Alternatively, parametric gates may also be compiled by interpolating control pulses. Modular gates thus obtained provide high-fidelity building blocks for quantum Hamiltonian simulations.
Ranking-Based Salient Object Detection and Depth Prediction for Shallow Depth-of-Field

Shallow depth-of-field (DoF), focusing on the region of interest by blurring out the rest of the image, is challenging in computer vision and computational photography. It can be achieved either by adjusting the parameters (e.g., aperture and focal length) of a single-lens reflex camera or computational techniques. In this paper, we investigate the latter one, i.e., explore a computational method to render shallow DoF. The previous methods either rely on portrait segmentation or stereo sensing, which can only be applied to portrait photos and require stereo inputs. To address these issues, we study the problem of rendering shallow DoF from an arbitrary image. In particular, we propose a method that consists of a salient object detection (SOD) module, a monocular depth prediction (MDP) module, and a DoF rendering module. The SOD module determines the focal plane, while the MDP module controls the blur degree. Specifically, we introduce a label-guided ranking loss for both salient object detection and depth prediction. For salient object detection, the label-guided ranking loss comprises two terms: (i) heterogeneous ranking loss that encourages the sampled salient pixels to be different from background pixels; (ii) homogeneous ranking loss penalizes the inconsistency of salient pixels or background pixels. For depth prediction, the label-guided ranking loss mainly relies on multilevel structural information, i.e., from low-level edge maps to high-level object instance masks. In addition, we introduce a SOD and depth-aware blur rendering method to generate shallow DoF images. Comprehensive experiments demonstrate the effectiveness of our proposed method.
Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation

Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer’s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.
Cybersecurity Awareness

Cybersecurity awareness can be viewed as the level of appreciation, understanding or knowledge of cybersecurity or information security aspects. Such aspects include cognizance of cyber risks and threats, but also appropriate protection measures.
Exploring Best Practices in Geoscience Education: Adapting a Video/Animation on Continental Rifting for Upper-Division Students to a Lower-Division Audience

Well-crafted and scientifically accurate videos and animations can be effective ways to teach dynamic Earth processes such as continental rifting, both in live course offerings as well as in online settings. However, a quick search of the internet reveals too few high-quality videos/animations describing deep Earth processes. We have modified a hybrid 10.5 min video/animation about continental rifting and the formation of new oceans and passive continental margins created for an upper-division geology audience, retailoring it for a lower-division geology audience. A key challenge in successfully modifying such resources is aligning the cognitive load that the video/animation imposes on students, in part related to the technical geoscientific jargon used in explaining such phenomena, with that which they encounter on these topics in their textbooks and classrooms. We used expert feedback obtained at a 2019 GeoPRISMS (Geodynamic Processes at Rifting and Subducting Margins) workshop in San Antonio to ensure the accuracy of the science content of the upper-division video. We followed this with a review of the terminology and language used in the video/animation, seeking to align the video narrative with the technical language used in introductory geology offerings, which we based on examining five current introductory geology textbooks and feedback from students in introductory geoscience courses. The revised introductory-level video/animation was piloted in an online introductory course, where it provided an improved conceptual understanding of the related processes of continental rifting, opening new oceans, and formation of passive continental margins.
Pre-Processed Tweets for Secure Capital Market Analysis Using Cloud

Huge crowds heading towards smart investment options need a secure and trustworthy environment to earn good profits. Twitter, a social networking platform, is a major source generating huge information on share market consortium. People get excited when they come across the tweets that specify the shares yielding huge profits within a short time. Due to this, they end up tweeting about their credentials and amount they are willing to invest. It paves a path for the intruders to access confidential data and leave the common man in danger by gaining access and misusing the information. Towards this end, the goal of this work is to address the challenge of providing better inputs to the customers interested to invest in the share market in a secure way to earn better returns on investment. In this work, as a first module, pre-processing techniques are used to remove the unwanted characters from tweets. In the second part, to enhance the security, encryption module is developed, and the data is then stored in Cassandra. It is observed from results that the time taken to encrypt 100,000 tweets after pre-processing is 500 msec, and the time taken to decrypt the same set of 100,000 tweets is 50 msec, respectively. This shows the effectiveness of the proposed work in terms of attaining better and fast outcomes for a huge set of tweets after filling the voids by pre-processing techniques.
A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks

Why heavily parameterized neural networks (NNs) do not overfit the data is an important long standing open question. We propose a phenomenological model of the NN training to explain this non-overfitting puzzle. Our linear frequency principle (LFP) model accounts for a key dynamical feature of NNs: they learn low frequencies first, irrespective of microscopic details. Theory based on our LFP model shows that low frequency dominance of target functions is the key condition for the non-overfitting of NNs and is verified by experiments. Furthermore, through an ideal two-layer NN, we unravel how detailed microscopic NN training dynamics statistically gives rise to a LFP model with quantitative prediction power.
Geometry of Banach Spaces: A New Route Towards Position Based Cryptography

Abstract In this work we initiate the study of position based quantum cryptography (PBQC) from the perspective of geometric functional analysis and its connections with quantum games. The main question we are interested in asks for the optimal amount of entanglement that a coalition of attackers have to share in order to compromise the security of any PBQC protocol. Known upper bounds for that quantity are exponential in the size of the quantum systems manipulated in the honest implementation of the protocol. However, known lower bounds are only linear. In order to deepen the understanding of this question, here we propose a position verification (PV) protocol and find lower bounds on the resources needed to break it. The main idea behind the proof of these bounds is the understanding of cheating strategies as vector valued assignments on the Boolean hypercube. Then, the bounds follow from the understanding of some geometric properties of particular Banach spaces, their type constants. Under some regularity assumptions on the former assignment, these bounds lead to exponential lower bounds on the quantum resources employed, clarifying the question in this restricted case. Known attacks indeed satisfy the assumption we make, although we do not know how universal this feature is. Furthermore, we show that the understanding of the type properties of some more involved Banach spaces would allow to drop out the assumptions and lead to unconditional lower bounds on the resources used to attack our protocol. Unfortunately, we were not able to estimate the relevant type constant. Despite that, we conjecture an upper bound for this quantity and show some evidence supporting it. A positive solution of the conjecture would lead to stronger security guarantees for the proposed PV protocol providing a better understanding of the question asked above.
Surface-driven Next-Best-View planning for exploration of large-scale 3D environments

In this paper, we propose a novel cluster-based informative path planning algorithm to simultaneously explore and inspect a large-scale unknown environment with an Unmanned Aerial Vehicle (UAV). Most of the existing methods address the surface inspection problem as a volume exploration problem, and consider that the surface has been scanned when the corresponding volume has been covered. Unfortunately, this approach may lead to inaccurate 3D models of the environment, and the UAV may not achieve global coverage. To overcome these critical limitations, we introduce a 3D reconstruction method based on TSDF (Truncated Signed Distance Function) mapping, which leverages the surfaces present in the environment to generate an informative exploration path for the UAV. A Probabilistic Roadmap planner, used to solve a TSP (Travelling Salesman Problem) over clusters of viewpoint configurations, ensures that the resulting 3D model is accurate and complete. Two challenging structures (a power plant and the Statue of Liberty) have been chosen to conduct realistic numerical experiments with a quadrotor UAV. Our results provide evidence that the proposed method is effective and robust.
Global Data Privacy Laws 2021: Despite COVID Delays, 145 Laws Show GDPR Dominance

The 50th anniversary in 2020 of the world’ s first data privacy law, the Datenschutzgesetz 1970 of the state of Hesse in Germany marks the first half century of their development. This article focuses on changes in the global development of these data privacy laws in 2019-20. In summary, the number of countries that have enacted data privacy laws rose from 132 to 145, and most of the 13 new laws are from 2019, before COVID-19 slowed down legislative activity world-wide. At least 23 other countries have official Bills for new laws in various stages of progress. During 2019-20, at least 13 countries have updated or replaced existing laws (almost always influenced by the EU’s GDPR). At least a further 11 have introduced Bills to do so. All of this adds up to nearly 60 countries world-wide where there was legislative activity in 2019-20 concerning data privacy, resulting in new or updated laws in 26 countries. All of these changes are detailed in the article, and in the two accompanying global Tables.
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling

Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. However, the most suitable positions for inferring different targets, <i>i.e</i>., the object category and boundaries, are generally different. Predicting all these targets from the same grid location thus may lead to sub-optimal results. In this paper, we analyze the suitable inference positions for object category and boundaries, and propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm. Our PDNet with the prediction decoupling mechanism encodes different targets separately in different locations. A learnable prediction collection module is devised with two sets of dynamic points, <i>i.e</i>., dynamic boundary points and semantic points, to collect and aggregate the predictions from the favorable regions for localization and classification. We adopt a two-step strategy to learn these dynamic point positions, where the prior positions are estimated for different targets first, and the network further predicts residual offsets to the positions with better perceptions of the object properties. Extensive experiments on the MS COCO benchmark demonstrate the effectiveness and efficiency of our method. With a single ResNeXt-64x4d-101-DCN as the backbone, our detector achieves 50.1 AP with single-scale testing, which outperforms the state-of-the-art methods by an appreciable margin under the same experimental settings. Moreover, our detector is highly efficient as a one-stage framework. Our code will be public.
Expressive Explanations of DNNs by Combining Concept Analysis with ILP

Explainable AI has emerged to be a key component for black-box machine learning approaches in domains with a high demand for reliability or transparency. Examples are medical assistant systems, and applications concerned with the General Data Protection Regulation of the European Union, which features transparency as a cornerstone. Such demands require the ability to audit the rationale behind a classifier's decision. While visualizations are the de facto standard of explanations, they come short in terms of expressiveness in many ways: They cannot distinguish between different attribute manifestations of visual features (e.g. eye open vs. closed), and they cannot accurately describe the influence of absence of, and relations between features. An alternative would be more expressive symbolic surrogate models. However, these require symbolic inputs, which are not readily available in most computer vision tasks. In this paper we investigate how to overcome this: We use inherent features learned by the network to build a global, expressive, verbal explanation of the rationale of a feed-forward convolutional deep neural network (DNN). The semantics of the features are mined by a concept analysis approach trained on a set of human understandable visual concepts. The explanation is found by an Inductive Logic Programming (ILP) method and presented as first-order rules. We show that our explanation is faithful to the original black-box model. The code for our experiments is available at https://github.com/mc-lovin-mlem/concept-embeddings-and-ilp/tree/ki2020.