Reviewerly/exHarmony huggingface dataset:
Notational Programming for Notebook Environments: A Case Study with Quantum Circuits

We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational programming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from 'recognition' of notations to 'reconfiguration' of practices and values around programming, and from 'sketching' to writing and drawing, or what we call 'notating.'
VRhook: A Data Collection Tool for VR Motion Sickness Research

Despite the increasing popularity of VR games, one factor hindering the industry's rapid growth is motion sickness experienced by the users. Symptoms such as fatigue and nausea severely hamper the user experience. Machine Learning methods could be used to automatically detect motion sickness in VR experiences, but generating the extensive labeled dataset needed is a challenging task. It needs either very time consuming manual labeling by human experts or modification of proprietary VR application source codes for label capturing. To overcome these challenges, we developed a novel data collection tool, VRhook, which can collect data from any VR game without needing access to its source code. This is achieved by dynamic hooking, where we can inject custom code into a game's run-time memory to record each video frame and its associated transformation matrices. Using this, we can automatically extract various useful labels such as rotation, speed, and acceleration. In addition, VRhook can blend a customized screen overlay on top of game contents to collect self-reported comfort scores. In this paper, we describe the technical development of VRhook, demonstrate its utility with an example, and describe directions for future research.
Identifying Critical Infrastructure in Imagery Data Using Explainable Convolutional Neural Networks

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types.
ILL-IDS: An incremental lifetime learning IDS for VANETs

Vehicle Ad Hoc Network (VANET) is one of the most important approaches for intelligent vehicles to communicate under complex road conditions. However, as a VANET is working under wireless and complex conditions, it is under the threat of various malicious attacks. One efficient solution to counter those attacks is the Intrusion Detection System (IDS), which can detect intrusions to the VANET based on the statistical machine learning method. However, most IDS solutions have a problem that they can only detect the attacks which have already known when the IDS is designed/trained. For unknown attacks, most IDSs become inefficient. A blockchain-based Lifetime Learning IDS (LL-IDS) framework has been designed to address this problem. It applies a blockchain to store uncertain data that an IDS cannot decide and is highly likely to be a new type of attacks. With the help of traditional security agencies such as universities, these uncertain data can be labelled to train the IDS model. The incremental learning models are shown to have a great potential in this scenario. In this paper, we introduces a novel IDS named as incremental IDS based on LL-IDS. Numerical experiments show that the computational time consumption and web payload could be decreased by applying the ILL-IDS to a public VANET dataset.
Towards Semantically Aware Word Cloud Shape Generation

Word clouds are a data visualization technique that showcases a subset of words from a body of text in a cluster form, where a word’s font size encodes some measure of its relative importance—typically frequency—in the text. This technique is primarily used to help viewers glean the most pertinent information from long text documents and to compare and contrast different pieces of text. Despite their popularity, previous research has shown that word cloud designs are often not optimally suited for analytical tasks such as summarization or topic understanding. We propose a solution for generating more effective visualization technique that shapes the word cloud to reflect the key topic(s) of the text. Our method automates the processes of manual image selection and masking required from current word cloud tools to generate shaped word clouds, better allowing for quick summarization. We showcase two approaches using classical and state-of-the-art methods. Upon successfully generating semantically shaped word clouds using both methods, we performed preliminary evaluations with 5 participants. We found that although most participants preferred shaped word clouds over regular ones, the shape can be distracting and detrimental to information extraction if it is not directly relevant to the text or contains graphical imperfections. Our work has implications on future semantically-aware word cloud generation tools as well as efforts to balance visual appeal of word clouds with their effectiveness in textual comprehension.
Little Garden: An augmented reality game for older adults to promote body movement

Physical activity is one of the most effective ways to help older adults stay healthy, but traditional training methods for older adults use single tasks and are boring, often making it difficult for the elderly to achieve good exercise results. In contrast to existing digital games, games based on augmented reality technology have the potential to promote physical activity in the elderly. This paper presents Little Garden, an interactive augmented-reality game designed for older adults. It uses projective augmented reality technology, physical card manipulation, virtual social scenarios to increase user engagement and motor initiation. The pilot data show that the game system promotes physical engagement and provides a good user experience. We believe that augmented reality technology provides a new approach to interface design for age-appropriate user-interface experiences.
Playful Learning with VR-SIMI Model: The Use of 360-Video as a Learning Tool for Nursing Students in a Psychiatric Simulation Setting

Abstract Educational institutions can elevate student perspective and activation so that playful learning is formed by looking for new teaching possibilities. Didactical methods need to provide a safe environment where students can focus on interpersonal interactions with patients while being aware of how their own emotions can influence their situational awareness and decisions. The authors believe that relevant scenarios in a 360-degree video format will be beneficial for nursing students, specifically in preparation for the clinical setting. The potential of 360-degree video in virtual reality (VR) gives the instructor flexibility to create systematic, experiential learning and shapes emotional learning in collaboration with students. 360-Degree video can be seen as a playful way to learn in new situations. Playfulness of this kind can affect teachers and students motivation, as well as the opportunity to promote learning. This field lacks studies exploring the use of 360-degree videos in psychiatric simulation settings. This chapter will provide knowledge about the practical use of 360-degree video in VR, insight into technical potential, as well as challenges. Background information on why this method is suitable for promoting nursing students’ competence in mental health work will be presented. Another function of this chapter is to give an introduction and inspire exploration of 360-degree video in VR in professional education, with particular focus on how this can be used as a tool for nursing students in psychiatric simulation settings, like the VR-SIMI model, which is discussed later in the chapter.
“I can’t do it”: A qualitative study exploring case and contact experiences with COVID-19 contact tracing

Low engagement in contact tracing for COVID-19 dramatically reduces its impact, but little is known about how experiences, environments and characteristics of cases and contacts influence engagement.We recruited a convenience sample of COVID-19 cases and contacts from the New Haven Health Department's contact tracing program for interviews about their contact tracing experiences. We analyzed transcripts thematically, organized themes using the Capability, Opportunity, Motivation, Behavior (COM-B) model, and identified candidate interventions using the linked Behavior Change Wheel Framework.We interviewed 21 cases and 12 contacts. Many felt physically or psychologically incapable of contact tracing participation due to symptoms or uncertainty about protocols. Environmental factors and social contacts also influenced engagement. Finally, physical symptoms, emotions and low trust in and expectations of public health authorities influenced motivation to participate.To improve contact tracing uptake, programs should respond to clients' physical and emotional needs; increase clarity of public communications; address structural and social factors that shape behaviors and opportunities; and establish and maintain trust. We identify multiple potential interventions that may help achieve these goals.
A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases

We present a neural dynamic model that perceptually grounds nested noun phrases, that is, noun phrases that contain further (possibly also nested) noun phrases as parts. The model receives input from the visual array and a representation of a noun phrase from language processing. It organizes a search for the denoted object in the visual scene. The model is a neural dynamic architecture of interacting neural populations which has clear interfaces with perceptual processes. It solves a set of theoretical challenges, including the problem of keeping a nested structure in short-term memory in a way that solves the problem of 2 and massive binding problem emphasized by Jackendoff. The model organizes a search for the objects that are referenced in that structure. We motivate the model, demonstrate simulation results, and discuss how it differs from related models.
Weakest preconditions in fibrations

Abstract Weakest precondition transformers are useful tools in program verification. One of their key properties is composability, that is, the weakest precondition predicate transformer (wppt for short) associated to program $f;\;g$ should be equal to the composition of the wppts associated to f and g . In this paper, we study the categorical structure behind wppts from a fibrational point of view. We characterize the wppts that satisfy composability as the ones constructed from the Cartesian lifting of a monad. We moreover show that Cartesian liftings of monads along lax slice categories bijectively correspond to Eilenberg–Moore monotone algebras. We then instantiate our techniques by deriving wppts for commonplace effects such as the maybe monad, the nonempty powerset monad, the counter monad, or the distribution monad. We also show how to combine them to derive the wppts appearing in the literature of verification of probabilistic programs.
Scalable fast benchmarking for individual quantum gates with local twirling

With the development of controllable quantum systems, fast and practical characterization for multi-qubit gates is essential for building high-fidelity quantum computing devices. The usual way to fulfill this requirement via randomized benchmarking asks for the complicated implementation of numerous multi-qubit twirling gates. How to efficiently and reliably estimate the fidelity of a quantum process remains an open problem. In this work, we propose a character-cycle benchmarking protocol and a character-average benchmarking protocol only using local twirling gates to estimate the process fidelity of an individual multi-qubit operation. Our protocols can characterize a large class of quantum gates including and beyond the Clifford group via the local gauge transformation, which forms a universal gate set for quantum computing. We numerically demonstrate our protocols for a non-Clifford gate -- controlled-$(TX)$ and a Clifford gate -- five-qubit quantum error-correcting encoding circuit. The numerical results show that our protocols can efficiently and reliably characterize the gate process fidelities. Compared with the cross-entropy benchmarking, the simulation results show that the character-average benchmarking achieves three orders of magnitude improvements in terms of sampling complexity.
Hierarchical remote preparation of arbitrary dimensional equatorial states

Hierarchical remote preparation of equatorial states has practical applications but has not been studied before. We first propose a deterministic protocol for two-dimensional equatorial states via a four-qubit cluster state. The sender can asymmetrically transmit the secret state to any one of the three agents who are ranked in terms of their authorities. A set of useful measurement bases is elaborately constructed which plays a key role in the protocol. Then the protocol is generalized to high-dimensional system. Further, taking advantage of cluster state’s symmetry, we give a universal protocol with multiple agents. The high-grade agent needs the assistance of the remaining high-grade agents as well as one of the low-grade agents. While the low-grade agent needs the cooperation of all the other agents. The agents’ recovery operators are expressed by a general formula which clearly reveals the relationship with the measurement results. Additionally, the impact of two kinds of noise is analyzed.
Highly Adiabatic Time-Optimal Quantum Driving at Low Energy Cost

Time-efficient control schemes for manipulating quantum systems are of great importance in quantum technologies, where environmental forces rapidly degrade the quality of pure states over time. In this Letter, we formulate an approach to time-optimal control that circumvents the boundary-value problem that plagues the quantum brachistochrone equation at the expense of relaxing the form of the control Hamiltonian. In this setting, a coupled system of equations, one for the control Hamiltonian and another one for the duration of the protocol, realizes an ansatz-free approach to quantum control theory. We show how driven systems, in the form of a Landau-Zener type Hamiltonian, can be efficiently maneuvered to speed up a given state transformation in a highly adiabatic manner and with a low energy cost.
The ambiguous feeling between “mine” and “not-mine” measured by integrated information theory during rubber hand illusion

Human body awareness is adaptive to context changes. The illusory sense of body ownership has been studied since the publication of the rubber hand illusion, where ambiguous body ownership feeling was first defined. Phenomenologically, the ambiguous body ownership is attributed to a conflict between feeling and judgement: it characterises a discrepancy between first- and third-person processes. Although Bayesian inference can explain this malleability of body image, it still fails to relate the subjective feeling to physiological data. This study attempts to explain subjective experience during rubber hand illusions by using integrated information theory (IIT). The integrated information [Formula: see text] in IIT measures the difference between the whole system and its subsystems. By analysing seven different time-series of physiological data representing a small body-brain system, we demonstrate that the integrity of the whole system during the illusion decreases, while the integrity of its subsystems increases. These general tendencies agree with many brain-image analyses and subjective reports; furthermore, we found that subjective ratings as ambiguous body ownership were associated with [Formula: see text]. Our result suggests that IIT can explain the general tendency of the sense of ownership illusions and individual differences in subjective experience during the illusions.
Task consolidation based power consumption minimization in cloud computing environment

Abstract Cloud Computing is playing a huge role in future technology. Further, with the explosive growth of the Internet and cloud computing, several service providers, such as Amazon, Microsoft, IBM, and Google, have expanded their data centers and rapidly deployed data centers in different places around the world to deliver various cloud computing services. However, several challenges are raised with the wide spread use of cloud environment such as power consumption, load balance, reliability, scalability, and security. This paper tackles the power consumption problem and presents an efficient algorithm, called Task Consolidation based Power Minimization (TCPM), to efficiently schedule tasks onto available resources of the cloud environment so as to minimize power consumption. In proposed TCPM algorithm, several benefits of the existing algorithms are enhanced and incorporated into the TCPM algorithm, where the best-fit procedure is used to achieve the best possible resource utilization and avoid wasting energy. The results of the proposed TCPM algorithm are compared with other recent algorithms such as FCFS, WWO, and MCT algorithms using the CloudSim toolkit.
Development of a Pill Dispenser: System Requirements and Product Architecture

The elderly population has been significantly increasing all over the world, causing an emerging need to create products that facilitate the day-to-day life of this population and that promote their independence in the domestic environment. This article focuses on the second phase of an investigation project which aims at creating a device that stores and dispenses pills automatically with the integration of a virtual assistant. In this second phase it was intended to define the system requirements and product architecture of the device, based on the state-of-the-art research, user experiences and business model defined in a previous paper. As a result, it was possible to discover the user needs and, consequently, the device’s requirements, which helped define the product architecture including a diagram of the system electronics and a basic representation of the device, aiding in the conception and development of the device.
Data Visualization in Hybrid Space—Constraints and Opportunities for Design

It is intended to investigate the hybrid space, as a determining attribute of the experience and interaction with augmented reality systems, to assess the potential and limitations of this space for information design, specifically in the context of data visualization. This paper presents a study which focused on the use of augmented reality in data visualization, in cases where there is no direct semantic relation between the virtual content and the real setting where the visualization will be displayed. For this study, a practice-based methodology was adopted, supported by the development, implementation and testing of a prototype of an augmented reality application for mobile devices. This prototype, which allows to visualize data related to design companies in Portugal, was assessed in real context. The data collected in this process sparked a reflection on the achieved results, but also on the challenges inherent to the application of this technology in the design of data visualizations.
A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

Statistical analysis of human daily activities contributes to time planning and health management. It also helps people make healthcare judgments and disease predictions when combined with big data technology. However, traditional methods for recognition of human daily activities based on vision and multisensors have limitations due to poor portability and weak capability in multiple activities detection. Herein, the development of a wearable human activity recognition (HAR) smart ring capable of recognizing at least 20 multi‐intensity activities, i.e., motions ranging from clean‐the‐desk to playing basketball, based on a microinertial measurement unit and a hierarchical decision algorithm is proposed. Users only need to wear the smart ring on the index finger of the right hand to accurately identify 20 common activities that are classified as light, vigorous, and fierce. A novel hierarchical decision algorithm using 70 features is proposed to improve the accuracy and speed of recognizing common human activities and provides a final recognition accuracy of 98.10% for 20 types of activities. This extremely portable, reliable, and high‐accuracy HAR solution is a significant advancement in providing real‐time quantitative data for personal lifestyle supervision, time planning, and healthcare management.
Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.