Reviewerly/exHarmony huggingface dataset:
Analog Quantum Simulation of Non-Condon Effects in Molecular Spectroscopy

In this work, we present a linear optical implementation for analog quantum simulation of molecular vibronic spectra, incorporating the non-Condon scattering operation with a quadratically small truncation error. Thus far, analog and digital quantum algorithms for achieving quantum speedup have been suggested only in the Condon regime, which refers to a transition dipole moment that is independent of nuclear coordinates. For analog quantum optical simulation beyond the Condon regime (i.e., non-Condon transitions) the resulting non-unitary scattering operations must be handled appropriately in a linear optical network. In this paper, we consider the first and second-order Herzberg-Teller expansions of the transition dipole moment operator for the non-Condon effect, for implementation on linear optical quantum hardware. We believe the method opens a new way to approximate arbitrary non-unitary operations in analog and digital quantum simulations. We report in-silico simulations of the vibronic spectra for naphthalene, phenanthrene, and benzene to support our findings.
Probabilities with Gaps and Gluts

Abstract Belnap-Dunn logic (BD), sometimes also known as First Degree Entailment, is a four-valued propositional logic that complements the classical truth values of True and False with two non-classical truth values Neither and Both . The latter two are to account for the possibility of the available information being incomplete or providing contradictory evidence. In this paper, we present a probabilistic extension of BD that permits agents to have probabilistic beliefs about the truth and falsity of a proposition. We provide a sound and complete axiomatization for the framework defined and also identify policies for conditionalization and aggregation. Concretely, we introduce four-valued equivalents of Bayes’ and Jeffrey updating and also suggest mechanisms for aggregating information from different sources.
From Leonardo to the Next Rembrandt – The Need for AI-Pessimism in the Age of Algorithms

Artificial Intelligence (AI) has become a part of our daily life, and “algorithmic creativity” has similarly gained a spotlight recently. From paintings to music, machines generate outputs that seem to comply with various prerequisites of copyright protection. The idea (and to some degree the need) to honour such achievements by legal protection has also emerged. This article demonstrates that contrary to the view of the proponents of an AI-copyright regime, time has not ripened for AI-copyright. The core elements of copyright law - namely the concept of authorship, originality and moral rights, as well as copyright’s history and incentives - are deeply rooted in an anthropocentric (although not only author-centric) world. Unless paradigm shift in copyright law, the lack of direct human element of an AI-generated output shall lead to the unavailability of copyright protection for these outputs. The article similarly highlights why the proposed alternative forms of copyright related rights or sui generis protection are doctrinally unfounded or practically unfit to provide for a sound solution that does not destroy the existing copyright regime and diminish the value of human creations.
Lindblad Tomography of a Superconducting Quantum Processor

As progress is made towards the first generation of error-corrected quantum computers, robust characterization and validation protocols are required to assess the noise environments of physical quantum processors. While standard coherence metrics and characterization protocols such as ${T}_{1}$ and ${T}_{2}$, process tomography, and randomized benchmarking are now ubiquitous, these techniques provide only partial information about the dynamic multiqubit loss channels responsible for processor errors, which can be described more fully by a Lindblad operator in the master equation formalism. Here, we introduce and experimentally demonstrate Lindblad tomography, a hardware-agnostic characterization protocol for tomographically reconstructing the Hamiltonian and Lindblad operators of a quantum noise environment from an ensemble of time-domain measurements. Performing Lindblad tomography on a small superconducting quantum processor, we show that this technique naturally builds on standard process tomography and ${T}_{1}/{T}_{2}$ measurement protocols, characterizes and accounts for state-preparation and measurement errors, and allows one to place bounds on the fit to a Markovian model. Comparing the results of single- and two-qubit measurements on a superconducting quantum processor, we demonstrate that Lindblad tomography can also be used to identify and quantify sources of crosstalk on quantum processors, such as the presence of always-on qubit-qubit interactions.
Hand-by-Hand Mentor: An AR based Training System for Piano Performance

Multimedia instrument training has gained great momentum benefiting from augmented and/or virtual reality (AR/VR) technologies. We present an AR-based individual training system for piano performance that uses only MIDI data as input. Based on fingerings decided by a pre-trained Hidden Markov Model (HMM), the system employs musical prior knowledge to generate natural-looking 3D animation of hand motion automatically. The generated virtual hand demonstrations are rendered in head-mounted displays and registered with a piano roll. Two user studies conducted by us show that the system requires relatively less cognitive load and may increase learning efficiency and quality.
Lightweight Low-Resolution Face Recognition for Surveillance Applications

Typically, real-world requirements to deploy face recognition models in unconstrained surveillance scenarios demand to identify low-resolution faces with extremely low computational cost. In the last years, several methods based on complex deep learning models have been proposed with promising recognition results but at a high computational cost. Inspired by the compactness and computation efficiency of lightweight deep face networks and their high accuracy on general face recognition tasks, in this work we propose to benchmark two recently introduced lightweight face models on low-resolution surveillance imagery to enable efficient system deployment. In this way, we conduct a comprehensive evaluation on the two typical settings: LR-to-HR and LR-to-LR matching. In addition, we investigate the effect of using trained models with downsampled synthetic data from high-resolution images, as well as the combination of different models, for face recognition on real low-resolution images. Experimental results show that the used lightweight face models achieve state-of-the-art results on low-resolution benchmarks with low memory footprint and computational complexity. Moreover, we observed that combining models trained with different degradations improves the recognition accuracy on low-resolution surveillance imagery, which is feasible due to their low computational cost.
Modeling Data Flow Constraints for Design-Time Confidentiality Analyses

With the increase in connectedness and the growing volume of data, ensuring confidentiality becomes increasingly critical. Data-driven analyses try to cope with this complexity by automatically verifying confidentiality at design time. However, confidentiality constraints are manifold. Thus, analyses limit the software architect's possibilities of expression or require them to use the underlying verification formalism directly. We propose a domain-specific language to enable architects to formulate data flow constraints using the terminology and abstraction of the architectural domain. We present a mapping of data flow constraints and results which is compliant to the transformation of the architecture and evaluated based on real-world scenarios.
What Are the Factors That Influence the Use of ICT in the Classroom by Teachers? Evidence From a Census Survey in Madrid

With the world becoming increasingly digitalized, ICT has a key role to play in the educational process. The aim of this research is to determine the personal characteristics of teachers, as well as those of the school and class environment, that make them more likely to use ICT in their classes. This research is especially interesting given that there is little pre-existing literature in this regard. For this purpose, we estimate a logistic model with data from the census survey for the Community of Madrid (2016-2017 academic year) of the individualized evaluation of students in the final evaluation of their fourth year of Compulsory Secondary Education for four different competences: Mathematics, social and civic competence, English and Spanish. Our results suggest that higher teacher motivation, greater use of ICT by students in school and at home and better-prepared teachers who require less ICT training is associated with more frequent use of ICT in the classroom in all four subjects. However, some of the determinants of the use of ICT differ according to the competence evaluated. Teachers working full time use ICT more frequently in the classroom, but only in Spanish and Mathematics. On the other hand, teachers with greater disruption of order in their class use ICT less frequently in social and civic competence and English. We also find that a lack of digital devices in the school is associated with lower uses of ICT in English, Spanish and Mathematics but is associated with a higher use in social and civic competence. Finally, we also find that private and semiprivate schools use more ICT than public schools in Mathematics, English and Spanish.
A Multi-Server Authentication Protocol Achieving Privacy Protection and Traceability for 5G Mobile Edge Computing

Mobile Edge Computing (MEC) is placed near mobile devices in the form of edge servers to meet the requirements of time-sensitive applications, providing low-latency computing and data storage. As user privacy issues are considered one of the biggest issues in the MEC environment, there are various approaches for anonymous authentication. However, these are not ready for commercial applications due to a lack of user traceability for rewards, service fees, penalties, and so on. This paper proposes a user authentication protocol in the MEC environment that satisfied the two conflicting security requirements of user anonymity and traceability. The proposed protocol provides anonymous mutual authentication between mobile devices and application servers operating on MEC platform, while ensures that the trusted MEC server can securely track users on behalf of service providers.
Wasserstein Generative Models for Patch-Based Texture Synthesis

This work addresses texture synthesis by relying on the local representation of images through their patch distributions. The main contribution is a framework that imposes the patch distributions at several scales using optimal transport. This leads to two formulations. First, a pixel-based optimization method is proposed, based on discrete optimal transport. We show that it generalizes a well-known texture optimization method that uses iterated patch nearest-neighbor projections, while avoiding some of its shortcomings. Second, in a semi-discrete setting, we exploit differential properties of Wasserstein distances to learn a fully convolutional network for texture generation. Once estimated, this network produces realistic and arbitrarily large texture samples in real time. By directly dealing with the patch distribution of synthesized images, we also overcome limitations of state-of-the-art techniques, such as patch aggregation issues that usually lead to low frequency artifacts (e.g. blurring) in traditional patch-based approaches, or statistical inconsistencies (e.g. color or patterns) in machine learning approaches.
Spam-E-Mail-Detection-Using-Advanced-Deep-Convolution-Neural-Network-Algorithms

The Spam e-mail is one of the noteworthy dangers on the planet today and has caused gigantic budgetary misfortunes. Even though the techniques for showdown are consistently being refreshed, the consequences of those strategies are not good at present. Also, Spam e-mail are developing at an alarming rate lately. Like this, more viable phishing recognition innovation is expected to control the danger of phishing emails. In this paper, we initially examined the email structure. At that point, in light of an improved intermittent convolutional neural systems (RCNN) model with staggered vectors and consideration instrument, we proposed another Spam e-mail recognition model named THEMIS, which is utilized to show emails at the email header, the email body, the character level, and the word level all the while. To assess the adequacy of THEMIS, we utilize a lopsided dataset that has reasonable proportions of phishing and genuine emails. The exploratory outcomes show that the general precision of THEMIS arrives at 99.848%. Then, the bogus positive rate (FPR) is 0.043%. High accuracy and low FPR guarantee that the modify can distinguish phishing emails with high likelihood and adjust out authentic emails as meagre as could be expected under the circumstances. This promising outcome is better than the current recognition techniques and confirms the adequacy of THEMIS in distinguishing Spam e-mail.
Analyzing Indian citizen's perspective towards government using wearable sensors to tackle COVID-19 crisis—A text analytics study

This research analyzes the Indian population's perspective on the government implementing wearable devices to tackle the ongoing COVID-19 pandemic. Using Text analytics, this study analyzes the sentiments of the Indian population regarding the government using wearable devices to control the COVID-19 pandemic. For this study, we have collected tweets of Indian citizens about the usage of wearable devices to tackle COVID-19. Sentimental analysis was done to understand the perception of Indian citizens regarding using wearable devices to control COVID-19. Topic modeling was done to understand the major negative issues the Indian public discuss about the usage of wearable sensors to control the COVID-19 crisis. Our analysis has shown that only 12% of the Indian population's sentiment towards the government implementing wearable devices to tackle COVID-19 is in a positive tone. Wearable devices being uncomfortable and privacy aspects are the main issues discussed by the Indian population in the tweets with negative sentiments. With just 12% of our population sample has a positive opinion about the government implementing wearable devices to tackle COVID-19, it will be hard for the government to achieve desired results. The Indian government should make more efforts in convincing its population about the benefits of implementing wearable devices to tackle COVID-19.
Improving text-to-image generation with object layout guidance

Abstract The automatic generation of realistic images directly from a story text is a very challenging problem, as it cannot be addressed using a single image generation approach due mainly to the semantic complexity of the story text constituents. In this work, we propose a new approach that decomposes the task of story visualization into three phases: semantic text understanding, object layout prediction, and image generation and refinement. We start by simplifying the text using a scene graph triple notation that encodes semantic relationships between the story objects. We then introduce an object layout module to capture the features of these objects from the corresponding scene graph. Specifically, the object layout module aggregates individual object features from the scene graph as well as averaged or likelihood object features generated by a graph convolutional neural network. All these features are concatenated to form semantic triples that are then provided to the image generation framework. For the image generation phase, we adopt a scene graph image generation framework as stage-I, which is refined using a StackGAN as stage-II conditioned on the object layout module and the generated output image from stage-I. Our approach renders object details in high-resolution images while keeping the image structure consistent with the input text. To evaluate the performance of our approach, we use the COCO dataset and compare it with three baseline approaches, namely, sg2im, StackGAN and AttnGAN, in terms of image quality and user evaluation. According to the obtained assessment results, our object layout guidance-based approach significantly outperforms the abovementioned baseline approaches in terms of the accuracy of semantic matching and realism of the generated images representing the story text sentences.
Dynamical Localization Simulated on Actual Quantum Hardware

Quantum computers are invaluable tools to explore the properties of complex quantum systems. We show that dynamical localization of the quantum sawtooth map, a highly sensitive quantum coherent phenomenon, can be simulated on actual, small-scale quantum processors. Our results demonstrate that quantum computing of dynamical localization may become a convenient tool for evaluating advances in quantum hardware performances.
Robust diagnostic classification via Q-learning

Abstract Machine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability , being able to audit and understand the decision function, and robustness , being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q -learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.
Colonoscopic 3D reconstruction by tubular non-rigid structure-from-motion

The visual examination of colonoscopic images fails to extract precise geometric information of the colonic surface. Reconstructing the 3D surface of the colon from colonoscopic image sequences may thus add valuable clinical information. We address this problem of extracting precise spatio-temporal 3D structure information from colonoscopic images. Using just the intrinsically calibrated monocular image stream, we develop a technique to compute the depth of certain feature points that have been tracked across images. Our method uses the prior knowledge of an approximate geometry of the colon, called the (TTP). It works by fitting a deformable cylindrical model to points reconstructed independently by non-rigid structure-from-motion (NRSfM), compromising between the data term and a novel tubular smoothing prior. Our method represents the first method ever to exploit a very weak topological prior to improve NRSfM. As such, it lies in-between standard NRSfM, which does not use a topological prior beyond the mere plane, and shape-from-template (SfT), which uses a very strong prior as a full deformable 3D object model. We validate our method on both synthetic images of tubular structures and real colonoscopic data. Our method improves the results obtained by existing NRSfM methods by 71.74% on average on synthetic data and succeeds in obtaining 3D reconstruction from a real colonoscopic sequence defeating the existing methods. Colonoscopic 3D reconstruction is a difficult problem, which is yet unresolved by the existing methods from computer vision. Our proposed dedicated NRSfM method and experiments show that the visual motion might be the right visual cue to use in colonoscopy.
SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science

Environmental scientific research is highly becoming data-driven and dependent on high performance computing infrastructures to process ever increasing large volume and diverse data sets. Consequently, there is a growing recognition of the need to share data, methods, algorithms, and infrastructure to make scientific research more effective, efficient, open, transparent, reproducible, accessible, and usable by different users. However, Earth Observations (EO) Open Science is still undervalued, and different challenges remains to achieve the vision of transforming EO data into actionable knowledge by lowering the entry barrier to massive-use Big Earth Data analysis and derived information products. Currently, FAIR-compliant digital repositories cannot fully satisfy the needs of EO users, while Spatial Data Infrastructures (SDI) are not fully FAIR-compliant and have difficulties in handling Big Earth Data. In response to these issues and the need to strengthen Open and Reproducible EO science, this paper presents SwissEnvEO, a Spatial Data Infrastructure complemented with digital repository capabilities to facilitate the publication of Ready to Use information products, at national scale, derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles.
Quality Estimation for Image Captions Based on Large-scale Human Evaluations

Automatic image captioning has improved significantly over the last few years, but the problem is far from being solved, with state of the art models still often producing low quality captions when used in the wild.In this paper, we focus on the task of Quality Estimation (QE) for image captions, which attempts to model the caption quality from a human perspective and without access to groundtruth references, so that it can be applied at prediction time to detect low-quality captions produced on previously unseen images.For this task, we develop a human evaluation process that collects coarse-grained caption annotations from crowdsourced users, which is then used to collect a large scale dataset spanning more than 600k caption quality ratings.We then carefully validate the quality of the collected ratings and establish baseline models for this new QE task.Finally, we further collect fine-grained caption quality annotations from trained raters, and use them to demonstrate that QE models trained over the coarse ratings can effectively detect and filter out lowquality image captions, thereby improving the user experience from captioning systems.