Reviewerly/exHarmony huggingface dataset:
An FPGA-Based Low Latency AWG for Superconducting Quantum Computers

This paper reports the development of a low-latency arbitrary waveform generator (AWG), which is suitable for the control and measurement of superconducting quantum computers. It can generate qubit control signals and measurement signals in real time. Compared with commercial AWG, our AWG has less latency and higher analog bandwidth. Compressing latency can reduce system feedback time, and higher analog bandwidth improves the system's integration by mounting more qubits on the readout line. The AWG integrates four high-speed digital-to-analog converters (DACs), phase-locked loop (PLL) circuits, field-programmable gate arrays (FPGAs), and related circuits into a standard PXI 3U board. The waveform generator module, communication interface module, clock module, and configuration module are realized utilizing the FPGA. The FPGA-based hardware architecture enables AWG to be flexibly configured to meet the various needs of quantum experiments. The four-channel AWG has a 2-GSa/s sampling rate, a 14-bit resolution, and 500 MHz bandwidth. The jitter in the AWG output waveform is approximately 3 ps; the latency of the AWG (the time from the feedback trigger received until a feedback pulse is generated) is approximately 55 ns. These designs form the basis of realizing control system for large-scale quantum computing.
Linear Decompositions for Multi-Valued Input Classification Functions

In a multi-valued input classification function, each input combination represents properties of an object, while the output represents the class of the object. Each variable may have different radix. In most cases, the functions are partially defined. To represent multi-valued variables, both one-hot and minimum-length encoding are considered. Experimental results using University of California Irvine (UCI) benchmark functions show that the one-hot approach results in fewer variables than the minimum-length approach with linear decompositions.
COVIDFREE App: The User-Enabling Contact Prevention Application

Governments all over the world are considering employing mobile contact tracing applications to automatically manage, trace, and investigate recent interactions of newly tested COVID-19 infected individuals. The prospective use of such applications has produced several debates surrounding confidentiality, security, data supervision, closeness approximation algorithms, and cyber-attack susceptibility. This paper discusses these concerns in the context of a novel smartphone application architecture, proposed here, that gives users more control and privacy during contact tracing. The proposed application is termed COVIDFREE App, which is aimed at enhancing users' situational awareness through communication of safe and unsafe locations. An overview of the app's architectural design and functionality are presented, including a newly developed Overall Risk Density Safety Factor calculation inspired by COVID-19 density risks and factors of users based on health requirements and several customizable user-specific scenarios. Finally, the paper outlines the benefit to funding and developing contact tracing apps, such as the COVIDFRE App, and discusses advantages of such applications including citizen awareness, relief, and preparedness.
Structural analysis of pile-foundation offshore platform

Abstract This paper takes the pile-foundation platform of Dagang Oilfield in paralic zones as the research object, provides a reference basis for further selection by analyzing the advantages and disadvantages of single platform type and multi-platform type. The key structural parameters including platform load calculation, deck bottom elevation determination and ultimate bearing capacity calculation of pressurized piles are analyzed and studied. The specific ideas of pile-foundation platform design are proposed, and the analysis process and important formulas of the methods used in the design are elaborated to give the research results, which has high reference significance for the old platform to extend its service life and the construction of the new platform.
An adaptive clustering algorithm based on boundary detection

To solve the problem that the partition clustering algorithm usually needs to specify the number of clusters artificially and the poor clustering effect on nonconvex datasets, this paper proposes an adaptive clustering algorithm based on boundary detection (BAC). BAC algorithm searches the boundary points of each cluster according to the global distribution characteristics of all samples in the sample space, then connects the boundary points to form the boundary shape closure of the cluster, and finally propagates the cluster labels of the boundary points to the non boundary points according to the nearest neighbor principle. BAC algorithm finds the number of clusters adaptively by searching the number of boundary shapes in the sample space, and it is not sensitive to the shape of clusters, so it can detect clusters with complex shapes. Experimental evaluation is carried out on a large number of datasets, and compared with K-means, K-means++ and DPC algorithms. The results show that the clustering performance of BAC algorithm is better than other algorithms.
A Quadruple Bottom Line of Sustainability Analysis of Ecofitting Design Concept

This paper presents a Quadruple Bottom Line Analysis of Sustainable Design Directions for Ecofitting, a sustainable solution for the large UK fleet of internal combustion engine cars that will soon be rendered non-compliant with fast approaching initiatives for Ultra Low Emission Vehicles. Ecofitting circular economy strategy goes beyond just electrification, opening an opportunity for new approaches to automotive design, and to cater for generational shifts in desirability. The need to understand subjective aspects related to aesthetic experience, aesthetic properties, values, worldviews, and tastes, initiated the development of an extended view on sustainability include these issues. The paper presents the analysis made with an expanded and revised Sustainable Design-Orienting tool [1]. The analysis pointed out that Ecofitting can have a significant impact in the automotive industry. At the same time, this study reveals the importance of the quadruple bottom line to develop tangible and intangible aspects of design.
Towards Black-Box Explainability with Gaussian Discriminant Knowledge Distillation

In this paper, we propose a method for post-hoc ex-plainability of black-box models. The key component of the semantic and quantitative local explanation is a knowledge distillation (KD) process which is used to mimic the teacher’s behavior by means of an explainable generative model. Therefore, we introduce a Concept Probability Density Encoder (CPDE) in conjunction with a Gaussian Discriminant Decoder (GDD) to describe the contribution of high-level concepts (e.g. object parts, color, shape). We argue that our objective function encourages both, an explanation given by a set of likelihood ratios and a measure to describe how far the explainer deviates from the training data distribution of the concepts. The method can leverage any pre-trained concept classifier that admits concept scores (e.g. logits) or probabilities. We demonstrate the effectiveness of the proposed method in the context of object detection utilizing the DensePose dataset.
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning

In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and improve sample efficiency. However, abundant information in self-supervised auxiliary tasks has been disregarded, since the representation learning part and the decision-making part are separated. To sufficiently utilize information in auxiliary tasks, we present a simple yet effective idea to employ self-supervised loss as an intrinsic reward, called Intrinsically Motivated Self-Supervised learning in Reinforcement learning (IM-SSR). We formally show that the self-supervised loss can be decomposed as exploration for novel states and robustness improvement from nuisance elimination. IM-SSR can be effortlessly plugged into any reinforcement learning with self-supervised auxiliary objectives with nearly no additional cost. Combined with IM-SSR, the previous underlying algorithms achieve salient improvements on both sample efficiency and generalization in various vision-based robotics tasks from the DeepMind Control Suite, especially when the reward signal is sparse.
Unifying Guided and Unguided Outdoor Image Synthesis

Given a source image, our goal is to synthesize novel images of the same scene under different conditions, which could include changes in the time of day, season, or weather conditions. We consider two variants, unguided and guided synthesis, both of which require a way to generate diverse output images that cover the range of possible conditions. For the former task, the layout of the output image should match the source image and the conditions should appear realistic. For the latter task, the conditions should match those of a provided auxiliary guidance image. We address both tasks simultaneously using a probabilistic formulation, with separate distributions for each task, and use an end-to-end training method. We draw samples from these distributions to synthesize plausible images of the source scene. We prepare a new large-scale dataset and propose three benchmark tasks. The dataset, benchmarks, and evaluation code are available at https://mvrl.github.io/un_guided.
Automatic Mask Detecion using Convolutional Neural Networks and Variational Autoencoder

The importance of proper hygienical behaivour is essential in today's word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.
Modal clustering on PPGMMGA projection subspace

Summary PPGMMGA is a projection pursuit (PP) algorithm aimed at detecting and visualising clustering structures in multivariate data. The algorithm uses the negentropy as PP index obtained by fitting Gaussian mixture models (GMMs) for density estimation and, then, exploits genetic algorithms (GAs) for its optimisation. Since the PPGMMGA algorithm is a dimension reduction technique specifically introduced for visualisation purposes, cluster memberships are not explicitly provided. In this paper a modal clustering approach is proposed for estimating clusters of projected data points. In particular, a modal EM algorithm is employed to estimate the modes corresponding to the local maxima in the projection subspace of the underlying density estimated using parsimonious GMMs. Data points are then clustered according to the domain of attraction of the identified modes. Simulated and real data are discussed to illustrate the proposed method and evaluate the clustering performance.
Intelligent Virtual Reality Tutoring Systems as a New Generation of Simulators: Requirements and Opportunities

This article explores the features and requirements of intelligent virtual reality tutoring systems - an innovative type of educational software. It also explores the applicability of these systems as educational simulators and classifies them with the help of existing typologies. The authors present a detailed discussion of immersive virtual learning environments, the penetration of artificial intelligence into this domain, and the effects of this penetration. The paper seems to be the first publication to survey intelligent virtual reality tutoring systems (IVRTSs). The results are our own IVRTS that can act as an educational simulator for kinematics as well as a set of recommendations on IVRTS design and implementation.
HSM-based Key Management Solution for Ethereum Blockchain

The security of distributed applications backed by blockchain technology relies mainly on keeping the associated cryptographic keys (i.e. private keys) in well-protected storage. Since they are the unique proof of ownership of the underlying digital assets. If the keys are stolen or lost, there is no way to recover the assets. The cold wallet is a good candidate for basic use cases, but it has a substantial challenge for more complex applications as it does not scale. Warm and hot wallets are more convenient options for blockchain-based solutions that aim to transact in a cloud environment. In this work, we focus on Hardware Security Module (HSM) based wallet. The HSM is the de-facto standard device designed to manage high-value cryptographic keys and to protect them against hacks. In this demonstration, we present an HSM-based working prototype that secures the entire life cycle of Ethereum public and private keys.
A Fight-to-Safety from Bitcoin to Stock Markets:Evidence from Cyber Attacks

We discover a novel flight-to-safety effect from cryptocurrency markets to stock markets. It occurs after a series of hacking attacks on crypto exchanges as greater uncertainty heightens investors' risk awareness and perception, which, in turn, induces herd-like behaviour in favor of safer stock markets over riskier cryptocurrency markets. We extensively examine this effect worldwide across 39 countries and confirm this novelty. This effect is amplified when investors' risk awareness is strengthened by frequent attacks. Intriguingly, the sentiment elicited from the attacks discussed on social media not only serves as a timely warning indicator for the forthcoming attack events but also measures the flight-to-safety pressure after attack events. We conclude that the collapse of investors’ confidence is the primary cause of such an effect. Through the lens of cyber attacks, we document how a shock in crypto markets is transmitted into stock markets through investors' flight-to-safety behavior.
A Fuzzy Semantic for BDI Logic

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Digitalization of education and the problem of continuity

The trends in the modern educational process related to digitalization, the spread of information and communication technologies, the translation of information into the language of computer programming, the use of graphic visualization techniques, computer graphics are considered. There is a tendency to increase the methodological developments prepared by the pedagogical community, dedicated to techniques, optimization of the translation of a large amount of information. The formation of an open digital educational environment is characterized as a factor of external management of the formation of learning youth. It is emphasized that this trend only partially solves the problem of professional development of young people. It is proved that continuity is possible if translation and inheritance are considered as complementary types of human activity. Inheritance, however, is a multi-level process of human interaction with the surrounding world, affecting the structures of the unconscious, and is meaningful in connection with the implementation of external activities by a person according to the norms and standards set by culture. Such a specific nature of the language makes us doubt the universal possibilities of digitizing the knowledge to be assimilated.
F-PENN— Forest path encoding for neural networks

Deep neural nets (DNNs) mostly tend to outperform other machine learning (ML) approaches when the training data is abundant, high-dimensional, sparse, or consisting of raw data (e.g., pixels). For datasets with other characteristics – for example, dense tabular numerical data – algorithms such as Gradient Boosting Machines and Random Forest often achieve comparable or better performance at a fraction of the time and resources. These differences suggest that combining these approaches has potential to yield superior performance. Existing attempts to combine DNNs with other ML approaches, which usually consist of feeding the output of the latter into the former, often do not produce positive results. We argue that this lack of improvement stems from the fact that the final classifications fail to provide the DNN with an understanding of the other algorithms’ decision-making process (i.e., its “logic”). In this study we present F-PENN, a novel approach for combining decision forests and DNNs. Instead of providing the final output of the forest (or its trees) to the DNN, we provide the paths traveled by each sample. This information, when fed to the neural net, yields significant improvement in performance. We demonstrate the effectiveness of our approach by conducting extensive evaluation on 56 datasets and comparing F-PENN to four leading baselines: DNNs, Gradient Boosted Decision Trees (GBDT), Random Forest and DeepFM. We show that F-PENN outperforms the baselines in 69%–89% of dataset and achieves an overall average error reduction of 16%–26%.
57‐1: Trends in Low‐Cost Displays for Emerging Applications

Demand for low‐cost displays with low power consumption and flexibility will increase dramatically as a wider range of devices incorporate electronic functionality. We assess the technological/ commercial readiness of each display type and application, forecasting that low‐cost, low‐power displays will be an approximately $1.2 bn market by 2030.
A Asymmetric Attention Siamese Network for Visual Object Tracking

To make the visual tracking aims algorithm more adaptive to shallow feature extraction, this paper proposes a Siamese network based trackers that can enhance the robustness of target trackers. Combined with attention model and feature fusion, a new asymmetric convolution structure is applied, which can effectively improve the performance of Siamese networks. Ablation experiments have proved that this model behaves well while features are of different depths. Finally, experiments are carried out on the OTB2015 benchmarks set, which show that performance of this algorithm exceeds other benchmark algorithms. Compared with the previous SiamFC, the success plot of this increased by 3.4%, and real-time tracking speed is also achieved. The advantages of the proposed algorithm are verified.
Conceptual Learning and Local Incommensurability: A Dynamic Logic Approach

Abstract In recent decades, the logical study of rational belief dynamics has played an increasingly important role in philosophy. However, the dynamics of concepts such as conceptual learning received comparatively little attention within this debate. This is problematic insofar as the occurrence of conceptual change (especially in the sciences) has been an influential argument against a merely logical analysis of beliefs. Especially Kuhn’s ideas about the incommensurability, i.e., untranslatability, of succeeding theories seem to stand in the way of logical reconstruction. This paper investigates conceptual change as model-changing operations similar to belief revision and relates it to the notion of incommensurability. I consider several versions of conceptual change and discuss their influences on the expressive power, translatability and the potential arising of incommensurability. The paper concludes with a discussion of animal taxonomy in Aristotle’s and Linnaeus’s work.
A Novel Adaptive Sampling Strategy for Deep Reinforcement Learning

Reinforcement learning, as an effective method to solve complex sequential decision-making problems, plays an important role in areas such as intelligent decision-making and behavioral cognition. It is well known that the sample experience replay mechanism contributes to the development of current deep reinforcement learning by reusing past samples to improve the efficiency of samples. However, the existing priority experience replay mechanism changes the sample distribution in the sample set due to the higher sampling frequency assigned to a specific transition, and it cannot be applied to actor-critic and other on-policy reinforcement learning algorithm. To address this, we propose an adaptive factor based on TD-error, which further increases sample utilization by giving more attention weight to samples of larger TD-error, and embeds it flexibly into the original Deep Q Network and Advantage Actor-Critic algorithm to improve their performance. Then we carried out the performance evaluation for the proposed architecture in the context of CartPole-V1 and 6 environments of Atari game experiments, respectively, and the obtained results either on the conditions of fixed temperature or annealing temperature, when compared to those produced by the vanilla DQN and original A2C, highlight the advantages in cumulative rewards and climb speed of the improved algorithms.
Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network

Predicting the optimum SWAP depth of a quantum circuit is useful because it informs the compiler about the amount of necessary optimization. Fast prediction methods will prove essential to the compilation of practical quantum circuits. In this paper, we propose that quantum circuits can be modeled as queuing networks, enabling efficient extraction of the parallelism and duration of SWAP circuits. To provide preliminary substantiation of this approach, we compile a quantum multiplier circuit and use a queuing network model to accurately determine the quantum circuit parallelism and duration. Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
Embracing Doubt: Teaching in a Post-digital Age

Abstract Since the concept of the teacher seems to be, that she or he is the one who owns knowledge—in contrast to students who don’t—it is understandable that teachers have doubts creating a situation out of those usual boundaries. In this chapter, it is argued that if we want to teach adequately for the post digital age, we have to embrace doubt. Doubt is a force that falls out of the usual teleological approach of teaching, because we don’t exactly know the outcome. This theoretical approach translates into practice—or better: is in itself a practical approach—because since one does not know which outcome an educational situation will have, he/she has improvise, embrace doubt, and deny classical power structures.