Reviewerly/exHarmony huggingface dataset:
Subjective and Objective Quality Assessments of Display Products

In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.
Modeling aggression propagation on social media

Cyberaggression has been studied in various contexts and online social platforms, and modeled on different data using state-of-the-art machine and deep learning algorithms to enable automatic detection and blocking of this behavior. Users can be influenced to act aggressively or even bully others because of elevated toxicity and aggression in their own (online) social circle. In effect, this behavior can propagate from one user and neighborhood to another, and therefore, spread in the network. Interestingly, to our knowledge, no work has modeled the network dynamics of aggressive behavior. In this paper, we take a first step towards this direction by studying propagation of aggression on social media using opinion dynamics. We propose ways to model how aggression may propagate from one user to another, depending on how each user is connected to other aggressive or regular users. Through extensive simulations on Twitter data, we study how aggressive behavior could propagate in the network. We validate our models with crawled and annotated ground truth data, reaching up to 80% AUC, and discuss the results and implications of our work.
Quasiconformal model with CNN features for large deformation image registration

&lt;p style='text-indent:20px;'&gt;Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More specifically, we propose a quasiconformal energy model with a novel fidelity term that incorporates the features extracted using a pre-trained CNN, thereby allowing us to obtain meaningful registration results without any guidance of prescribed landmarks. Moreover, unlike many prior image registration methods, the bijectivity of our method is guaranteed by quasiconformal theory. Experimental results are presented to demonstrate the effectiveness of the proposed method. More broadly, our work sheds light on how rigorous mathematical theories and practical machine learning approaches can be integrated for developing computational methods with improved performance.&lt;/p&gt;
A no‐reference blurred colourful image quality assessment method based on dual maximum local information

Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No-reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing-based auto-focussing and maximum local information theories, a no-reference blurred colourful IQA method based on Dual Maximum Local Information is proposed here. First, a window extraction method that combines the maximum gradient with local entropy is proposed to obtain the region of interest (ROI) for subsequent processing. Second, an improved maximum gradient method that leverages information from different channel images is presented to calculate the maximum gradient variation within the ROI for final sharpness score. Experimental results illustrated that the proposed method has better performance under various measurements compared with the state-of-the-art methods on LIVE, CSIQ, TID2008, TID2013, VCL@FER, IVC image databases.
Sensorimotor serial dependencies in head movements

On average, we redirect our gaze with a frequency at about 3 Hz. In real life, gaze shifts consist of eye and head movements. Much research has focused on how the accuracy of eye movements is monitored and calibrated. By contrast, little is known about how head movements remain accurate. I wondered whether serial dependencies between artificially induced errors in head movement targeting and the immediately following head movement might recalibrate movement accuracy. I also asked whether head movement targeting errors would influence visual localization. To this end, participants wore a head-mounted display and performed head movements to targets, which were displaced as soon as the start of the head movement was detected. I found that target displacements influenced head movement amplitudes in the same trial, indicating that participants could adjust their movement online to reach the new target location. However, I also found serial dependencies between the target displacement in trial n-1 and head movements amplitudes in the following trial n. I did not find serial dependencies between target displacements and visuomotor localization. The results reveal that serial dependencies recalibrate head-movement accuracy. NEW & NOTEWORTHY Head movements are recalibrated by serial dependencies by errors between consecutive trials. Head movements are subject to a regression of the average target location.
Accuracy Analysis for Logistic Regression Algorithm and Random Forest Algorithm to Detect Frauds in Mobile Money Transaction

Aim: The main motto of the study is to detect the frauds in mobile money transactions using logistic regression and random forest algorithms and comparing their accuracy. Materials and Methods: Logistic regression (N=10) and random forest algorithm(N=10) was iterated 20 times and detected the frauds. Results and Discussion: Random forest has significantly better accuracy (99.6%) compared to logistic regression (92.6%). The statistical significance of random forest algorithm (p&lt;0.018 Independent sample T-test) is high. Conclusion: Within the limits of this study, random forest algorithm offers better accuracy to detect frauds in mobile money transactions.
Self-Focused and Other-Focused Health Concerns as Predictors of the Uptake of Corona Contact Tracing Apps: Empirical Study

Background Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial. Objective This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one’s own health and concern to unknowingly infect others. Methods A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables. Results Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one’s own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that “too much” concern leads to defensive reactions and reduces app uptake. Conclusions As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed.
The legal content of a white paper for an ICO (initial coins offering)

Clear legal content of white papers provides certainty, stability and trust in the relations between all the participants in initial coins offering (ICO) procedures and contributes to the successful introduction of blockchain technologies in business. A white paper defines the relationship and interaction between a regulated social reality and program code, which is by itself not susceptible to legal regulation. This article investigates the legal conditions of the white papers that describe the projects of ICO. The author provides a legal analysis of numerous white papers published during ICO implementation around the world. Based on extensive data on implemented ICO projects, the correlation between the legal content of a white paper and the amount of funds collected as a result of the ICO is considered. Having analysed the determinants of the white paper legal content, the author suggests the legal provisions that any ICO white paper should have.
DeformRS: Certifying Input Deformations with Randomized Smoothing

Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e.g. translations, rotations, etc. Current input deformation certification methods either (i) do not scale to deep networks on large input datasets, or (ii) can only certify a specific class of deformations, e.g. only rotations. We reformulate certification in randomized smoothing setting for both general vector field and parameterized deformations and propose DeformRS-VF and DeformRS-Par, respectively. Our new formulation scales to large networks on large input datasets. For instance, DeformRS-Par certifies rich deformations, covering translations, rotations, scaling, affine deformations, and other visually aligned deformations such as ones parameterized by Discrete-Cosine-Transform basis. Extensive experiments on MNIST, CIFAR10, and ImageNet show competitive performance of DeformRS-Par achieving a certified accuracy of 39\% against perturbed rotations in the set [-10 degree, 10 degree] on ImageNet.
A robust method for image stitching

We propose a novel method for large-scale image stitching that is robust against repetitive patterns and featureless regions in the imagery. In such cases, state-of-the-art image stitching methods easily produce image alignment artifacts, since they may produce false pairwise image registrations that are in conflict within the global connectivity graph. Our method augments the current methods by collecting all the plausible pairwise image registration candidates, among which globally consistent candidates are chosen. This enables the stitching process to determine the correct pairwise registrations by utilizing all the available information from the whole imagery, such as unambiguous registrations outside the repeating pattern and featureless regions. We formalize the method as a weighted multigraph whose nodes represent the individual image transformations from the composite image, and whose sets of multiple edges between two nodes represent all the plausible transformations between the pixel coordinates of the two images. The edge weights represent the plausibility of the transformations. The image transformations and the edge weights are solved from a non-linear minimization problem with linear constraints, for which a projection method is used. As an example, we apply the method in a large-scale scanning application where the transformations are primarily translations with only slight rotation and scaling component. Despite these simplifications, the state-of-the-art methods do not produce adequate results in such applications, since the image overlap is small, which can be featureless or repetitive, and misalignment artifacts and their concealment are unacceptable.
Multilinear monogamy relations for multiqubit states

The monogamy of entanglement means that entanglement cannot be freely shared. In 2014, Oliveira et al. [T. R. de Oliveira, M. F. Cornelio, and F. F. Fanchini, Phys. Rev. A 89, 034303 (2014)] proposed a monogamy relation in the linear version and considered it in terms of entanglement of formation. Here we generalize the above version and consider a multilinear monogamy relation for a multiqubit system in terms of entanglement of formation and concurrence. Based on the above results, we present an entanglement criterion for genuine entangled states; also we consider the absolutely maximally entangled states and present what an absolutely maximally entangled state is for a three-qubit system. Last, we apply our results to a three-qubit pure state in terms of quantum discord.
ELEMENTS OF AI - TEACHING THE BASICS OF AI TO EVERYONE IN SWEDEN

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 2568-2572ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.0559Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
A Differentiable Language Model Adversarial Attack on Text Classifiers

Transformer models play a crucial role in state of the art solutions to problems arising in the field of natural language processing (NLP). They have billions of parameters and are typically considered as black boxes. Robustness of huge Transformer-based models for NLP is an important question due to their wide adoption. One way to understand and improve robustness of these models is an exploration of an adversarial attack scenario: check if a small perturbation of an input invisible to a human eye can fool a model. Due to the discrete nature of textual data, gradient-based adversarial methods, widely used in computer vision, are not applicable per se. The standard strategy to overcome this issue is to develop token-level transformations, which do not take the whole sentence into account. The semantic meaning and grammatical correctness of the sentence are often lost in such approaches In this paper, we propose a new black-box sentence-level attack. Our method fine-tunes a pre-trained language model to generate adversarial examples. A proposed differentiable loss function depends on a substitute classifier score and an approximate edit distance computed via a deep learning model. We show that the proposed attack outperforms competitors on a diverse set of NLP problems for both computed metrics and human evaluation. Moreover, due to the usage of the fine-tuned language model, the generated adversarial examples are hard to detect, thus current models are not robust. Hence, it is difficult to defend from the proposed attack, which is not the case for others. Our attack demonstrates the highest decrease of classification accuracy on all datasets(on AG news: 0.95 without attack, 0.89 under SamplingFool attack, 0.82 under DILMA attack).
Applying the Proportionality Principle to COVID-19 Certificates

With the SARS-CoV-2 pandemic entering its second year, public and private actors alike grow eager to achieve some semblance of normality. In this context, the idea of “vaccination passports” or “immunity certificates” as a means of resuming social and economic activity has been gaining momentum all around the world. This article aims to provide a legal analysis of this initiative through the lens of the proportionality principle. A proportionality test is conducted in order to determine whether the degree of infringement of the human rights implicated is balanced by the potential of a certification system to mitigate the risks of the virus. The results from this analysis show that the targeted aims can be achieved through already existing measures with a lesser impact on civil and fundamental human rights. Moreover, in a context of uncertainty around the immunopathology of COVID-19, the introduction of these certificates presents ethical and scientific challenges, which lead us to believe that this measure is unlikely to play a central role in stopping the spread of the disease, and it could set the pace for a dangerous precedent, allowing for extensive discrimination and exacerbating already existing inequalities and disparities.
Quantum state preparation protocol for encoding classical data into the amplitudes of a quantum information processing register's wave function

We present a protocol for encoding $N$ real numbers stored in $N$ memory registers into the amplitudes of the quantum superposition that describes the state of $\log_2N$ qubits. This task is one of the main steps in quantum machine learning algorithms applied to classical data. The protocol combines partial CNOT gate rotations with probabilistic projection onto the desired state. The number of additional ancilla qubits used during the implementation of the protocol, as well as the number of quantum gates, scale linearly with the number of qubits in the processing register and hence logarithmically with $N$. The average time needed to successfully perform the encoding scales logarithmically with the number of qubits, in addition to being inversely proportional to the acceptable error in the encoded amplitudes. It also depends on the structure of the data set in such a way that the protocol is most efficient for non-sparse data.
Design of Goal-Oriented Artifacts from Morphological Taxonomies: Progression from Descriptive to Prescriptive Design Knowledge

Morphological Taxonomies are a widely popular tool in Information Systems to systematically deconstruct an artifact into designable dimensions and characteristics. Subsequently, these taxonomies have engraved in them knowledge about the design of artifacts, i.e., descriptive design knowledge. Most studies producing morphological taxonomies refrain from giving prescriptive advice about the design, i.e., the specific morphological configuration of an artifact, but rather stay descriptive. The paper proposes a framework for knowledge and artifact transformation originating in morphological taxonomies and ending in design principles. We develop a framework that assists researchers and practitioners by showing clear paths on transforming descriptive design knowledge engraved in taxonomies to prescriptive knowledge as design principles.
Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Reservoir computing refers to a computational framework based on recurrent neural networks that can process time-series data. In an echo state network (ESN), which is a type of reservoir computing framework, the reservoir consists of a recursive network of artificial neurons with nonlinear activation functions. A model predictive control (MPC) technique can determine the control signals by solving the optimization problem of a system using the finite-time domain of each control period. However, real-time optimization cannot be achieved unless the optimal control problem can be solved within the next control period. To overcome this limitation, we propose a new control method based on MPC that explicitly incorporates the predicted disturbance of a time-varying trajectory using ESN to achieve the active vibration control of hybrid electric vehicle (HEV) powertrains. Once the ESN has been trained, the associated MPC explicitly satisfies the constraints over a moving horizon without further training. Instead of completing the real-time optimization within the control period, ESN predicts the future disturbance and applies it to the MPC in the future control period. Based on the predicted future disturbance, the system calculates the optimal control signals required for the future. Thus, real-time control can be realized because the optimal signals are determined before the subsequent control period occurs. The proposed method can be implemented in MPC even if the control period is too short to optimize as long as the disturbance can be reasonably measured and predicted. In this study, the simulation approach was demonstrated using the engine start condition in an HEV powertrain. The importance of this study is that the limitation of MPC relevant to real-time optimization can be relaxed by applying our proposed method.
Effects of a nuclear disturbed environment on a quantum free space optical link

This manuscript investigates the potential effect of a nuclear-disturbed atmospheric environment on the signal attenuation of a ground/satellite transmitter/receiver system for both classical optical and quantum communications applications. Attenuation of a signal transmitted through the rising nuclear cloud and the subsequently transported debris is modeled climatologically for surface-level detonations of 10 kt, 100 kt, and 1 Mt. Attenuation statistics were collected as a function of time after detonation. These loss terms were compared to normal loss sources such as clouds, smoke from fires, and clear sky operation. Finally, the loss was related to the degradation of transmitted entanglement derived from Bayesian mean estimation.
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 2021

This paper describes the offline and simultaneous speech translation (ST) systems developed at AppTek for IWSLT 2021.Our offline ST submission includes the direct endto-end system and the so-called posterior tight integrated model, which is akin to the cascade system but is trained in an end-to-end fashion, where all the cascaded modules are end-to-end models themselves.For simultaneous ST, we combine hybrid automatic speech recognition (ASR) with a machine translation (MT) approach whose translation policy decisions are learned from statistical word alignments.Compared to last year, we improve general quality and provide a wider range of quality/latency trade-offs, both due to a data augmentation method making the MT model robust to varying chunk sizes.Finally, we present a method for ASR output segmentation into sentences that introduces a minimal additional delay.