Reviewerly/exHarmony huggingface dataset:
Constructing training set using distance between learnt graphical models of time series data on patient physiology, to predict disease scores

Interventional endeavours in medicine include prediction of a score that parametrises a new subject’s susceptibility to a given disease, at the pre-onset stage. Here, for the first time, we provide reliable learning of such a score in the context of the potentially-terminal disease VOD, that often arises after bone marrow transplants. Indeed, the probability of surviving VOD, is correlated with early intervention. In our work, the VOD-score of each patient in a retrospective cohort, is defined as the distance between the (posterior) probability of a random graph variable—given the inter-variable partial correlation matrix of the time series data on variables that represent different aspects of patient physiology—and that given such time series data of an arbitrarily-selected reference patient. Such time series data is recorded from a pre-transplant to a post-transplant time, for each patient in this cohort, though the data available for distinct patients bear differential temporal coverage, owing to differential patient longevities. Each graph is a Soft Random Geometric Graph drawn in a probabilistic metric space, and the computed inter-graph distance is oblivious to the length of the time series data. The VOD-score learnt in this way, and the corresponding pre-transplant parameter vector of each patient in this retrospective cohort, then results in the training data, using which we learn the function that takes VOD-score as its input, and outputs the vector of pre-transplant parameters. We model this function with a vector-variate Gaussian Process, the covariance structure of which is kernel parametrised. Such modelling is easier than if the score variable were the output. Then for any prospective patient, whose pre-transplant variables are known, we learn the VOD-score (and the hyperparameters of the covariance kernel), using Markov Chain Monte Carlo based inference.
Authentication by Keystroke Dynamics: The Influence of Typing Language

Keystroke dynamics is a biometric method that uses a subject’s typing patterns for authentication or identification. In this paper we investigate typing language as a factor influencing an individual’s keystroke dynamics. Specifically, we discern whether keystroke dynamics is contingent on the spatial arrangement of letters on the keyboard, or alternatively, whether it is influenced by the linguistic characteristics inherent to the language being used. For this purpose, we construct a new dataset called the Bilingual Keystroke Dynamics Dataset in two languages: English and Arabic. The results show that the authentication system is not contingent on the spatial arrangement of the letters, and is primarily influenced by the language being used, and a system that is used by bilingual users must take into account that each user should have two profiles created, one for each language. An average equal error rate of 0.486% was achieved when enrolling in English and testing on Arabic, and 0.475% when enrolling in Arabic and testing on English.
Experimental Evaluation of Highly Accurate 3D Measurement Using Stereo Camera and Line Laser

This paper proposes a method to improve the accuracy of 3D measurement of a stereo camera by marking a measured object using a line laser. Stereo cameras are commonly used for 3D measurement, but the accuracy of 3D measurement is affected by the amount of texture. Therefore, a new measurement system combining a stereo camera and a line laser is developed. The accuracy of 3D measurement with a stereo camera is improved by using a line laser to mark arbitrary points on the measured object and measuring the marked points, regardless of the amount of texture on the measured object. Because the laser is only used to mark points on the measurement target, calibration is not required with the stereo camera. Experimental evaluation showed that our proposed method can obtain millimeters.
Image encryption based on chaotic maps and random scan patterns

We propose a novel image encryption scheme that utilizes chaotic maps and random scan patterns to protect image content. Our scheme includes three stages: pixel permutation via random scan patterns, block pixel diffusion, and block permutation. In the first stage, the input image is partitioned into overlapping blocks, and random scan patterns controlled by chaotic maps are exploited to scramble pixels of blocks. In the second stage, content-based keys are generated from the input image, and the content-based keys and chaotic maps are used to calculate secure matrices for achieving diffusion. The block pixel diffusion is conducted by performing element-wise XOR operation between blocks and their corresponding secure matrices. In the third stage, the image is partitioned into non-overlapping blocks, and all blocks are randomly permuted to further improve security. Key space analysis shows that the key space of the proposed scheme is bigger than those of some existing schemes. Experiments on standard test images are conducted by using some well-known metrics, such as histogram, χ2 test, correlation coefficient, entropy, the number of pixels change rate, the unified average changing intensity, and peak signal-to-noise ratio. The results demonstrate that the proposed scheme reaches the state-of-the-art performance in security and speed.
Prioritizing Aesthetic Touch: Interpreting Historical Textiles with Digital Embroidery

A lack of accessible exhibitions for Blind and low-vision (BLV) visitors in art museums and historical sites is a barrier to equitable participation. The design and fabrication of accessible materials can be time-consuming and expensive for many institutions. We describe our work using digital embroidery machines to interpret historical textiles to provide accessible interpretations for BLV museum visitors and tactile learners. Our work focuses on designing solutions that that are informative, durable, and enjoyable to touch, while affordable to produce. Through a collaborative design process with tactile graphics experts in museums we create prototypes of historical textiles that meet these criteria. We describe our design process and offer fabrication guidelines for patterns and materials.
A Demonstration of RASSAR: Room Accessibility and Safety Scanning in Augmented Reality

In this demo paper, we introduce RASSAR, a mobile AR application for semi-automatically identifying, localizing, and visualizing indoor accessibility and safety issues using LiDAR and real-time computer vision. Our prototype supports four classes of detection problems: inaccessible object dimensions (e.g., table height), inaccessible object positions (e.g., a light switch out of reach), the presence of unsafe items (e.g., scissors), and the lack of proper assistive devices (e.g., grab bars). RASSAR’s design was informed by a formative interview study with 18 participants from five key stakeholder groups, including wheelchair users, blind and low vision participants, families with young children, and caregivers. Our envisioned use cases include vacation rental hosts, new caregivers, or people with disabilities themselves documenting issues in their homes or rental spaces and planning renovations. We present key findings from our formative interviews, the design of RASSAR, and results from an initial performance evaluation.
Understanding Noise Sensitivity through Interactions in Two Online Autism Forums

Noise sensitivity is challenging for many people and particularly prevalent for autistic people. In an effort to understand these experiences from the perspective of autistic people and the opportunities for assistive technologies to support noise-sensitive people, we analyzed the posts and responses of noise-sensitive autistic people and their allies in two online forums. These forums were intentionally chosen for their inclusion and centering of autistic people’s experiences as we strove to understand this space from their perspective. Our analysis of both original discussion forum posts about noise sensitivity and comments in response to them reveal three themes: sharing experiences of noise sensitivity, managing noise sensitivity, and disclosing autism. Our work contributes a foundational understanding of the lived experiences of autistic people who are noise-sensitive and the implications for the design of assistive technology to support noise sensitivity.
Conveying Uncertainty in Data Visualizations to Screen-Reader Users Through Non-Visual Means

Incorporating uncertainty in data visualizations is critical for users to interpret and reliably draw informed conclusions from the underlying data. However, visualization creators conventionally convey the information regarding uncertainty in data visualizations using visual techniques (e.g., error bars), which disenfranchises screen-reader users, who may be blind or have low vision. In this preliminary exploration, we investigated ways to convey uncertainty in data visualizations to screen-reader users. Specifically, we conducted semi-structured interviews, finding that these users prefer to obtain statistical information on uncertainty expressed in plain language, conveyed holistically with avenues to explore the data further in a drilled-down manner. To support screen-reader users in extracting information about uncertainty in online data visualizations, we utilized our findings to enhance VoxLens—an open-source JavaScript plug-in that makes online data visualizations accessible to screen-reader users.
Designing a Human-Centered Intelligent System to Monitor &amp; Explain Abnormal Patterns of Older Adults

Older adult care technologies are increasingly explored to support the independent living of older adults by monitoring their abnormal activities and informing caregivers to provide intervention if necessary. However, the adoption of these technologies remains challenging due to several factors (e.g. lack of usability). In this work, we present a human-centered, intelligent system for older adult care. Our proposed designs of the system were created based on the findings from a focus group session with caregivers. This system monitors the abnormal activities of an older adult using wireless motion sensors and machine learning models. In addition, unlike previous work that only notifies an outcome of activity recognition and abnormal detection models to a caregiver, the system supports interactive dialogue responses to explain the abnormal activities of an older adult to a caregiver and allow the caregiver to elicit additional information about the older adult and the older adult to proactively share his/her status with the caregiver for an adequate intervention.
Understanding Blind and Low Vision Users' Attitudes Towards Spatial Interactions in Desktop Screen Readers

Desktop screen readers as a web navigation mechanism for BLV users are tedious and frozen in time, especially in the face of richer ways of presenting spatial information such as tactile and touchscreen devices. In our work, we consider what it means to create and evaluate systems that can present a similarly rich, spatial interaction mechanism plugged into existing screen reader paradigms. We present a formative study conducted with SpaceNav, a custom screen reader that utilizes spatial input and output to navigate two different web applications. We present results from this study, and discuss a new browser extension we are implementing based on our formative study feedback to more robustly test spatial interactions in the context of real world websites. To close, we describe our goals for evaluating the new web extension in a future study.
Enhancing Textual Accessibility for Readers with Dyslexia through Transfer Learning

This paper explores automated modification of text to make it more accessible for people with dyslexia, a reading disorder affecting a significant percentage of the global population. The modifications are both in terms of changing the appearance of text and simplification of the words, grammar, and length of textual material. For simplification of text, we built a dataset with original and dyslexia-friendly text verified by human readers that it improve their reading experience by 27% on average. Then we developed a pipeline to generate dyslexia-friendly text automatically using transfer learning. The model learns styles appropriate for dyslexic users and generates dyslexia-friendly text from arbitrary textual data, which is easier for people with dyslexia to read and interpret.
AI temporal planning for energy smart buildings

Abstract Buildings are responsible for about one-third of industrialised countries’ overall energy consumption and greenhouse gas emissions. As if this was not enough, recently, energy prices significantly increased and affected all economic areas. Making buildings more efficient and effective is the step needed toward cost reductions. Key enablers of cost-effectiveness are leveraging batteries, awareness of and adaptability to energy prices, and integrating powerful reasoning techniques to optimally and flexibly operate buildings. Researchers have tackled many of these aspects using a variety of approaches. Whereas a less investigated one is that of AI planning to coordinate actions and save energy in buildings. However, generating plans based on signals of energy prices and leveraging batteries is still an open research problem. To address this high-potential aspect, we engineer an AI planning system for improving the energy-cost effectiveness in buildings by coordinating the building’s operation based on day-ahead prices and the use of a battery, all without sacrificing the comfort of building occupants. We propose to exploit temporal planning due to its powerful modelling and reasoning features, especially in explicitly addressing time. We evaluate the effectiveness of the system in several scenarios with varying building environmental conditions. We compare the energy cost from using our planning system to a baseline cost, where we record a reduction of 43rage in favour of our system.
Concept-Centric Software Development: An Experience Report

Developers have long recognized the importance of the concepts underlying the systems they build, and the primary role that concepts play in shaping user experience. To date, however, concepts have tended to be only implicit in software design with development being organized instead around more concrete artifacts (such as wireframes and code modules).
Upholding “the educational” in education: Schooling beyond learning and the market

Abstract This article argues that schooling’s driving purpose should be to educate . Given heightening global crises and the potential of education to respond, we agree with the spirit and focus of UNESCO’s (2021) A new social contract for education intervention. Education/schooling should be motivated by progressive, critical visions to contribute to more sustainable and just human and planetary futures. Embodied-affective-cognitive technologically-mediated processes of becoming educated , however, are not a force that can smash injustice or ecologically destructive capitalism. Educationally speaking, there is no shortcut to cultivating students as “change agents” for sustainable futures. Hannah Arendt’s essay “The crisis in education” (2006) is instructive in clarifying the function of schooling and in categorically distinguishing adults from children, education from politics, and education from learning. While human learning proliferates in multiple ways independent of existential/ethical mooring, education ultimately requires committed adults spending time with, and socioemotionally and intellectually supporting, children to deepen their understanding of the world and others, giving meaning and significance to their/our lives as part of larger collectives called upon to sustain and renew a common world.
Corrigendum: Ring attractor bio-inspired neural network for social robot navigation

[This corrects the article DOI: 10.3389/fnbot.2023.1211570.].
Collecting Garbage on the Blockchain

We present a garbage collector that is specifically designed for a WebAssembly-based blockchain, such as the Internet Computer. Applications on the blockchain implement smart contracts that may have indefinitely long lifetime and may hold substantial monetary value. This imposes a different set of requirements for garbage collection compared to traditional platforms. In this paper, we explain the differences and show how our garbage collector optimizes towards these goals.
Forecast Center

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s). Additional informationNotes on contributorsTim VasquezTIM VASQUEZ is a former Air Force forecaster and author of Digital Atmosphere, a weather forecasting software program. He lives in Norman, Oklahoma, where he keeps busy as a weather software developer and hosts the Forecast Lab educational channel at youtube.com/ForecastLab.
Cloning and Beyond: A Quantum Solution to Duplicate Code

Quantum computers are becoming a reality. The advantage of quantum computing is that it has the potential to solve computationally complex problems in a fixed amount time, independent of the size of the problem. However, the kinds of problems for which these computers are a good fit, and the ways to express those problems, are substantially different from the kinds of problems and expressions used in classical computing. Quantum annealers, in particular, are currently the most promising and available quantum computing devices in the short term. However, they are also the most foreign compared to classical programs, as they require a different kind of computational thinking. In order to ease the transition into this new world of quantum computing, we present a novel quantum approach to a well-known software problem: code clone detection. We express code clone detection as a subgraph isomorphism problem that is mapped into a quadratic optimization problem, and solve it using a DWave quantum annealing computer. We developed a quantum annealing algorithm that compares Abstract Syntax Trees (AST) and reports an energy value that indicates how similar they are.
Improving Garbage Collection Observability with Performance Tracing

Debugging garbage collectors for performance and correctness is notoriously difficult. Among the arsenal of tools available to systems engineers, support for one of the most powerful, tracing, is lacking in most garbage collectors. Instead, engineers must rely on counting, sampling, and logging. Counting and sampling are limited to statistical analyses while logging is limited to hard-wired metrics. This results in cognitive friction, curtailing innovation and optimization.
General quantum matrix exponential dimensionality-reduction framework based on block encoding

As a general framework, matrix exponential dimensionality reduction (MEDR) deals with the small-sample-size problem that appears in linear dimensionality-reduction (DR) algorithms. High complexity is the bottleneck in this type of DR algorithm because one has to solve a large-scale matrix exponential generalized eigenproblem. To address it, here we design a general quantum algorithm framework for MEDR based on the block-encoding technique. This framework is configurable, that is, by selecting suitable methods to design the block encodings of the data matrices, a series of efficient quantum algorithms can be derived from this framework. Specifically, by constructing the block encodings of the data matrix exponentials, we solve the generalized eigenproblem and then obtain the digital-encoded quantum state corresponding to the compressed low-dimensional data set, which can be directly utilized as the input state for other quantum machine learning tasks to overcome the curse of dimensionality. As applications, we apply this framework to four linear DR algorithms and design their quantum algorithms, which all achieve a polynomial speedup in the dimension of the sample over their classical counterparts.
Overcoming diagnostic challenges of artificial intelligence in pathology and radiology: Innovative solutions and strategies

The advent of artificial intelligence (AI) has brought about significant changes in the fields of pathology and radiology, particularly in the area of diagnostic accuracy. Although AI has enormous potential for enhancing the precision and effectiveness of diagnosis, it also presents an array of challenges. This review article examines the diagnostic challenges of AI in pathology and radiology. The article begins by giving a general review of AI and its potential applications in pathology and radiology. It then discusses the challenges posed by AI in the areas of data quality, generalization, interpretability, and hardware limitations. The article also explores the ethical and regulatory implications of AI in diagnostic settings, including issues of bias and transparency. Finally, the article offers potential solutions to address these challenges, such as standardization of AI algorithms, data sharing initiatives, saliency mapping, adversarial training of algorithms, cloud computing, edge computing, hybrid approaches, and increased collaboration between human experts and AI systems. Overall, this review highlights the critical importance of addressing the diagnostic challenges of AI in pathology and radiology to make sure AI is able to achieve its potential to enhance patient care.
Spin-augmented observables for efficient photonic quantum error correction

We demonstrate that the spin state of solid-state emitters inside micropillar cavities can serve as measure qubits in syndrome measurements. The photons, acting as data qubits, interact with the spin state in the microcavity and the total state of the system evolves conditionally due to the resulting circular birefringence. By performing a quantum nondemolition measurement on the spin state the syndrome of the optical state can be obtained. Furthermore, due to the symmetry of the interaction, we can alternatively choose to employ the optical states as measure qubits. This protocol can be adapted to various resource requirements, including spectral discrepancies between the data qubits, by considering entangled measure qubits. Finally, we discuss how spin-systems with dissimilar characteristic energies can still be entangled with high levels of fidelity and tolerance to cavity losses in the strong coupling regime.
Composing Turing Machines in FSM

For Computer Science students, designing Turing machines is a Herculean task. Formal Languages and Automata Theory textbooks aid students by introducing a graphical notation for Turing machine composition. The difficulty of the task remains unchanged, because design principles are not emphasized and students rarely have the opportunity to program their designs in a textual programming language which allows them to write unit tests. To aid students that are trained as programmers, FSM--a domain-specific language for the Automata Theory classroom--has been developed. Using FSM, students design, program, validate, and establish the correctness of their Turing machines. Once they are familiar with Turing machines, students are introduced to Turing machine composition much like they are introduced to function composition when they learn to design programs. To compose Turing machines in FSM, there is an embedded domain-specific language that students may use. In this manner, students' training in programming is made relevant in the course. This article discusses how students are taught to design, program, validate, and establish the correctness of composed Turing machines.