Reviewerly/exHarmony huggingface dataset:
Goal-oriented adaptive sampling under random field modelling of response probability distributions

In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We consider cases where the spatial variation of these response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions, and in particular to a spatial extension of the logistic Gaussian model. The considered models deliver probabilistic predictions of response distributions at candidate points, allowing for instance to perform (approximate) posterior simulations of probability density functions, to jointly predict multiple moments and other functionals of target distributions, as well as to quantify the impact of collecting new samples on the state of knowledge of the distribution field of interest. In particular, we introduce adaptive sampling strategies leveraging the potential of the considered random distribution field models to guide system evaluations in a goal-oriented way, with a view towards parsimoniously addressing calibration and related problems from non-linear (stochastic) inversion and global optimisation.
Low overhead universality and quantum supremacy using only Z control

We consider a model of quantum computation we call ``varying $Z$'' ($\mathrm{V}Z$), defined by applying controllable $Z$-diagonal Hamiltonians in the presence of a uniform and constant external $X$ field, and prove that it is universal, even in one dimension. Universality is demonstrated by construction of a universal gate set with $O(1)$ depth overhead. We then use this construction to describe a circuit whose output distribution cannot be classically simulated unless the polynomial hierarchy collapses, with the goal of providing a low-resource method of demonstrating quantum supremacy. The $\mathrm{V}Z$ model can achieve quantum supremacy in $O(n)$ depth in one dimension, equivalent to the random circuit sampling models despite a higher degree of homogeneity: it requires no individually addressed $X$ control.
Complex Contexts and Subtle Actions: Design and Evaluation of a Virtual Coach for Utilitarian Cycling

The role of HCI to support cyclists has been researched for many years. However, there has been relatively little work on the interaction with and acting within real-world traffic situations. We collaborated with professional cycling instructors to understand experiences of (novice) cyclists in complex real-world urban traffic situations and existing coaching and teaching strategies. Building on this understanding, we designed a system informed by these strategies: an on-the-bike Virtual Cycling Coach (VCC), a system aware of its user’s actions and context that delivers coaching interventions in appropriate moments. In a qualitative in-the-wild study with 25 participants we evaluated if the VCC – as Wizard-of-Oz prototype – can offer a coaching experience. Our findings show that the VCC enables processes such as reflection and learning comparable to conventional coaching settings. We discuss limitations and implications for similar designs in complex, highly context-dependent realms.
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks

Small neural networks with a constrained number of trainable parameters, can be suitable resource-efficient candidates for many simple tasks, where now excessively large models are used. However, such models face several problems during the learning process, mainly due to the redundancy of the individual neurons, which results in sub-optimal accuracy or the need for additional training steps. Here, we explore the diversity of the neurons within the hidden layer during the learning process, and analyze how the diversity of the neurons affects predictions of the model. As following, we introduce several techniques to dynamically reinforce diversity between neurons during the training. These decorrelation techniques improve learning at early stages and occasionally help to overcome local minima faster. Additionally, we describe novel weight initialization method to obtain decorrelated, yet stochastic weight initialization for a fast and efficient neural network training. Decorrelated weight initialization in our case shows about 40% relative increase in test accuracy during the first 5 epochs.
Practical and configurable network traffic classification using probabilistic machine learning

Network traffic classification that is widely applicable and highly accurate is valuable for many network security and management tasks. A flexible and easily configurable classification framework is ideal, as it can be customized for use in a wide variety of networks. In this paper, we propose a highly configurable and flexible machine learning traffic classification method that relies only on statistics of sequences of packets to distinguish known, or approved, traffic from unknown traffic. Our method is based on likelihood estimation, provides a measure of certainty for classification decisions, and can classify traffic at adjustable certainty levels. Our classification method can also be applied in different classification scenarios, each prioritizing a different classification goal. We demonstrate how our classification scheme and all its configurations perform well on real-world traffic from a high performance computing network environment.
Capsule Networks with Routing Annealing

Capsule Networks overcome some shortcomings of convolutional neural networks organizing neurons into groups of capsules. Capsule layers are dynamically connected by means of an iterative routing mechanism, which models the connection strengths between capsules from different layers. However, whether routing improves the network performance is still object of debate. This work tackles this issue via Routing Annealing (RA), where the number of routing iterations is annealed at training time. This proposal gives some insights on the effectiveness of the routing for Capsule Networks. Our experiments on different datasets and architectures show that RA yields better performance over a reference setup where the number of routing iterations is fixed (even in the limit case with no routing), especially for architectures with fewer parameters.
Generalized Second-Order Value Iteration in Markov Decision Processes

Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore it may take a large number of iterations to converge to the optimal solution. Successive relaxation is a popular technique that can be applied to solve a fixed point equation. It has been shown in the literature that, under a special structure of the MDP, successive over-relaxation technique computes the optimal value function faster than standard value iteration. In this work, we propose a second order value iteration procedure that is obtained by applying the Newton-Raphson method to the successive relaxation value iteration scheme. We prove the global convergence of our algorithm to the optimal solution asymptotically and show the second order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach.
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound

In the PAC-Bayesian literature, the C-Bound refers to an insightful relation between the risk of a majority vote classifier (under the zero-one loss) and the first two moments of its margin (i.e., the expected margin and the voters’ diversity). Until now, learning algorithms developed in this framework minimize the empirical version of the C-Bound, instead of explicit PAC-Bayesian generalization bounds. In this paper, by directly optimizing PAC-Bayesian guarantees on the C-Bound, we derive self-bounding majority vote learning algorithms. Moreover, our algorithms based on gradient descent are scalable and lead to accurate predictors paired with non-vacuous guarantees.
Mobile app helps trainees manage emergencies at the bedside

Abstract Background Although emergency departments (ED) have standardized guidelines for low‐frequency, high‐acuity diagnoses, they are not immediately accessible at the bedside, and this can cause anxiety in trainees and delay patient care. This problem is exacerbated during events like COVID‐19 that require the rapid creation, iteration, and dissemination of new guidelines. Methods Physician innovators used design thinking principles to develop EM Protocols (EMP), a mobile application that clinicians can use to immediately view guidelines, contact consultants (e.g., cath lab activation), and access code‐running tools. The project became an institutional high priority, because it helps EM trainees and off‐service rotators manage low‐frequency, high‐acuity emergencies at the point of care, and its COVID‐19 guidelines can be rapidly updated and disseminated in real time. Results This intervention was deployed across two academic medical centers during the COVID‐19 surge. Nearly 300 ED clinicians have downloaded EMP, and they have interacted with the app over 5,400 times. It continues to be used regularly, over 12 months after the initial surge. Since the app was received positively, there are efforts to build in additional adult and pediatric guidelines. Discussion Digital health tools like EMP can serve as invaluable adjuncts for managing acute, life‐threatening emergencies at the point of care. They can benefit trainees during normal day‐to‐day operations as well as scenarios that cause large‐scale operational disruptions, such as natural disasters, mass casualty events, and future pandemics.
Vanishing boosted weights: A consistent algorithm to learn interpretable rules

Learning compact but highly accurate models that help in human decision-making is challenging. Most such scoring systems were constructed by human experts using some heuristics. In this contribution, we propose a principled method with theoretical guarantees to learn interpretable simple rules. We introduce Vanishing Boosted Weights (VBW) approach which is a corrective fine-tuning procedure on decision stumps. It is a simple method which surprisingly was never investigated. We propose its extension, Corrective Federated Averaging VBW, that is practical in a federated learning scenario. We illustrate by our numerical experiments both on simulated and real data that the novel approaches are competitive compared to the state-of-the-art methods, and outperform them.
Bandwidth control of the biphoton wavefunction exploiting spatio-temporal correlations

In this work we study the spatio-temporal correlations of photons produced by spontaneous parametric down conversion. In particular, we study how the waists of the detection and pump beams impact on the spectral bandwidth of the photons. Our results indicate that this parameter is greatly affected by the spatial properties of the detection beam, while not as much by the pump beam. This allows for a simple experimental implementation to control the bandwidth of the biphoton spectra, which only entails modifying the optical configuration to collect the photons. Moreover, we have performed Hong–Ou–Mandel interferometry measurements that also provide the phase of the biphoton wavefunction, and thereby its temporal shape. We explain all these results with a toy model derived under certain approximations, which accurately recovers most of the interesting experimental details.
Organization оf Qualitative Education of Music Students in the Conditions of Distance Education

The relevance of the chosen direction of research is determined by the need to improve the higher education system in accordance with the requirements of postmodernism (synergy of educational systems; non-linear construction of the educational process in order to take into account the needs of each individual, change in the nature of pedagogical interaction to ensure the symmetry of relations of all participants in the educational process, change in the status of knowledge and, as a result, the emergence of the need to improve professional competence throughout life, the orientation of education to actualizing the creative potential of future specialists, etc.). Based on the results of research by domestic and foreign scientists, the features of distance education, its advantages and disadvantages are established, which are taken into account in further scientific research. Required solutions in order to ensure high-quality distance education for music students, as well as identified topical issues: creation of an organizational model of distance learning; selection of appropriate means and tools, taking into account the peculiarities of professional training of music students, as well as substantiation of the system of methodological support of distance learning in accordance with the cycle of disciplines and the purpose of the educational program. It was noted that distance education makes it possible to introduce innovative technologies into the process of professional training of musicians, to build an individual educational trajectory, taking into account the needs of students.
Genre Determining Prediction: Non-Standard TAM Marking in Football Language

German and French football language display tense-aspect-mood (TAM) forms which differ from the TAM use in other genres. In German football talk, the present indicative may replace the pluperfect subjunctive. In French reports of football matches, the imperfective past may occur instead of a perfective past tense-aspect form. We argue that the two phenomena share a functional core and are licensed in the same way, which is a direct result of the genre they occur in. More precisely, football match reports adhere to a precise script and specific events are temporally determined in terms of objective time. This allows speakers to exploit a secondary function of TAM forms, namely, they shift the temporal perspective. We argue that it is on the grounds of the genre that comprehenders predict the deviating forms and are also able to decode them. In various corpus studies, we explore the functioning of these phenomena in order to gain insights into their distribution, grammaticalization and their functioning in discourse. Relevant factors are Aktionsart properties, rhetorical relations and their interaction with other TAM forms. This allows us to discuss coping mechanisms on the part of the comprehender. We broaden our understanding of the phenomena, which have only been partly covered for French and up to now seem to have been ignored in German.
Splitting Choice and Computational Complexity Analysis of Decision Trees

Some theories are explored in this research about decision trees which give theoretical support to the applications based on decision trees. The first is that there are many splitting criteria to choose in the tree growing process. The splitting bias that influences the criterion chosen due to missing values and variables with many possible values has been studied. Results show that the Gini index is superior to entropy information as it has less bias regarding influences. The second is that noise variables with more missing values have a better chance to be chosen while informative variables do not. The third is that when there are many noise variables involved in the tree building process, it influences the corresponding computational complexity. Results show that the computational complexity increase is linear to the number of noise variables. So methods that decompose more information from the original data but increase the variable dimension can also be considered in real applications.
Authenticity and Nominal Detection of Indonesian Banknotes Using ROI and CNN

A banknote is an economic tool used as a generally accepted medium of exchange. However, it is prone to counterfeiting, such as in Indonesia, in which the case of banknotes counterfeiting continues to increase. Hence, some computer-based applications have been developed to detect the authenticity of banknotes to reduce counterfeiting cases. Unfortunately, they focus on either nominal detection only or authenticity detection only. Besides, they use noiseless datasets and augmentation processes to be subject to overfitting or prediction errors. In this paper, the Indonesian banknote detection system is developed to identify both authenticity and nominal using the region of interest (ROI) and convolutional neural network (CNN). The evaluation shows that the authenticity model achieves a high accuracy of 95%, while the nominal classification model achieves an accuracy of 99%.
Educational E-escape Room as an Educational Method of Media Literacy Training for Future Teachers During the COVID-19 Pandemic

In the face of the global crisis of the Covid-19 pandemic, the training of future teachers, just like the whole formal education, has mostly moved to e-learning or blended-learning. This also applies to increasing media literacy knowledge, skills and competences. Media education - understood as developing an informed, critical, selective and comprehensive approach to media, information and digital competences with regard to the multilateral creation of messages and the use of technological tools - has become an even clearer necessity in the age of the Covid-19 pandemic. The global situation calls for new innovative methods and techniques that can be implemented in distance learning. The article presents studies on the use of a digital escape room in the distance training of future teachers in the field of media competences. The studies apply participatory observation as part of virtual ethnography and analysis of audio-visual works in the form of digital designs of escape rooms made by students of various faculties. The main problem comprises three specific research problems: 1. What thematic content dominated in the escape room designs and how did it relate to the media competence category in the Catalogue of Media, Information and Digital Competences? 2. What are the advantages and disadvantages of designing a digital escape room in media education as perceived by the active creators/authors of such rooms? 3. How do passive players rate the potential of the escape room method in extending media literacy? The results of the exploration and analyses indicate that the educational digital escape room has a great potential for effective increase in media literacy among young people and young adults.
In-depth Analysis of Side-Channel Countermeasures for CRYSTALS-Kyber Message Encoding on ARM Cortex-M4

A variety of post-quantum cryptographic schemes are currently undergoing standardization in the National Institute of Standards and Technology’s post-quantum cryptography standardization process. It is well known from classical cryptography that actual implementations of cryptographic schemes can be attacked by exploiting side-channels, e.g. timing behavior, power consumption or emanation in the electromagnetic field. Although several of the reference implementations currently in the third and final standardization round are – to some extent – implemented in a timing-constant fashion, resistance against other side-channels is not taken into account yet. Implementing sufficient countermeasures, however, is challenging. We therefore exemplarily examine CRYSTALS-Kyber, which is a lattice-based key encapsulation mechanism currently considered as a candidate for standardization. By analyzing the power consumption side-channel during message encoding we develop four more and compare six different implementations with an increasing degree of countermeasures. We show that introducing randomization countermeasures is crucial as all examined implementations aiming at reducing the leakage by minimizing the Hamming distance of the processed intermediate values only are vulnerable against single-trace attacks when implemented on an ARM Cortex-M4.
A Path Planning Method for Multi-robot Formation Based on Improved Q-Learning

This paper studies a path planning method for multiple robots in unknown environment. Multiple robots adopt the leader-following formation method. For the Q-learning algorithm used by the leader robot, the Q-table is initialized by prior information of environment and the idea of filling concave obstacles is proposed. Then the strategy of choosing actions is improved by simulated annealing algorithm, which changes the greedy factor in real time according to the Q-learning. The follower robot uses an improved gravitational potential field method to follow the leader robot. The simulation results show that the improved algorithm is effective and multiple robots can plan an optimum path to reach the destination with this method.
2 An Analysis on Non-linear Dimension Reduction Techniques

Rapid use of social media for communication and information sharing, use of world wide web as a huge information repository, advancement in data and image capturing techniques have drastically increased the volume and size of the data. Analyzing, processing and searching such a huge volume of data is a complex task. This also reduces the accuracy as well as diminishes performance. To overcome these problems dimension reduction techniques are used to reduce the size of data and features without the loss of valuable information. Dimension of features can be reduced by projecting the features to a low dimensional space or using feature selection or feature encoding techniques. These techniques can be linear or non-linear depending on the relationship between data in higher and lower dimensions. In this chapter, a detailed analysis has been performed on some of the non-linear dimensionality reduction techniques and their applications.
Problems In The Organization Of Student Practical Work Placement: Stakeholders’ Viewpoints

This paper reviews the current situation of the organization of students’ practical work placement as an educational component that ensures the improvement of training quality in terms of the formation of practical skills and further employment of graduates in Russia. Work placement is considered here from the viewpoints of two main stakeholders: the university and employers. This work contributes to the existing body of literature and knowledge concerning university-employer cooperation and outlines directions for improving the efficiency of practical work placement. The methods chosen for the research on the current issues and perspectives of the cooperation are as follows: analysis of documents, literature review, survey of organizations, interviews with stakeholders, focus group discussions with representatives of host organizations, university administration and the faculty. Data on the current state of affairs in the field of organization of industrial practice at the university and host companies are collected. The problems of preparing, conducting, supporting and evaluating the results of practical work placement have been identified, and the needs for improvement in the following areas have been revealed: documentation and organizational support of practice at the university; forms of work with students during practice in companies; university-company interaction.