Reviewerly/exHarmony huggingface dataset:
Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation

Background Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events. Objective The aim of this study was to develop a machine learning–based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing. Methods The algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314). Results Among the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample. Conclusions We developed and validated a machine learning–based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.
Characterization and classification of optimal LCD codes

Linear complementary dual (LCD) codes are linear codes that intersect with their dual trivially. We give a characterization of LCD codes over $$\mathbb {F}_q$$ having large minimum weights for $$q \in \{2,3\}$$ . Using the characterization, for arbitrary n, we determine the largest minimum weights among LCD [n, k] codes over $$\mathbb {F}_q$$ , where $$(q,k) \in \{(2,4), (3,2),(3,3)\}$$ . Moreover, for arbitrary n, we give a complete classification of optimal LCD [n, k] codes over $$\mathbb {F}_q$$ , where $$(q,k) \in \{(2,3), (2,4), (3,2),(3,3)\}$$ .
A comparative study of new and existing segmentation techniques

Abstract Image segmentation is the most crucial part in image processing techniques. Numerous segmentation techniques are used to segment digital images into smaller regions called segments, consisting of sets of pixels in order to analyze important information from the images. Segmentation simplifies the process of information retrieval from the region of interest (ROI).It helps in converting the digital image into more relevant information and easier to analyze. This paper presents a comparative analysis of existing segmentation techniques and its modification to form new segmentation techniques to overcome some of the drawbacks of the existing image segmentation approaches.
COTAF: Convergent Over-the-Air Federated Learning

Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchanging their trained model with a central server, allowing to tune a global model without having the users share their possibly private data. A major challenge in FL is to reduce the bandwidth and energy consumption due to the repeated transmissions of large volumes of data by a large number of users over the wireless channel. Recently, over-the-air (OTA) FL has been suggested to achieve this goal. In this setting, all users transmit their data signal simultaneously over a Multiple Access Channel (MAC), and the computation is done over the wireless channel. In this paper, we develop a novel convergent OTA FL (COTAF) algorithm, which induces precoding and scaling upon transmissions to gradually mitigate the effect of the noisy channel, thus facilitating FL convergence. We analyze the convergence of COTAF to the loss minimizing model theoretically, showing its ability to achieve a convergence rate similar to that achievable over error-free channels. Our simulations demonstrate the improved convergence of COTAF for training using non-synthetic datasets.
Ethical Challenges of Using Virtual Environments in the Assessment and Treatment of Psychopathological Disorders

Clinicians are increasingly interested in the potential of virtual environments for research and praxes. Virtual environments include both immersive and non-immersive simulations of everyday activities. Moreover, algorithmic devices and adaptive virtual environments allow clinicians a medium for personalizing technologies to their patients. There is also increasing recognition of social virtual environments that connect virtual environments to social networks. Although there has been a great deal of deliberation on these novel technologies for assessment and treatment, less discourse has occurred around the ethical challenges that may ensue when these technologies are applied clinically. In this paper, some of the ethical issues involved in the clinical use of novel technologies are discussed.
Bipartite entanglement of decohered mixed states generated from maximally entangled cluster states

In this work, we investigate the bipartite entanglement of decohered mixed states generated from maximally entangled cluster states of [Formula: see text] qubits physical system. We introduce the disconnected cluster states for an ensemble of [Formula: see text] non-interacting qubits and we give the corresponding separable density matrices. The maximally entangled states can be generated from disconnected cluster states, by assuming that the dynamics of the multi-qubit system is governed by a quadratic Hamiltonian of Ising type. When exposed to a local noisy interaction with the environment, the multi-qubit system evolves from its initial pure maximally entangled state to a decohered mixed state. The decohered mixed states generated from bipartite, tripartite and multipartite maximally entangled cluster states are explicitly expressed and their bipartite entanglements are investigated.
Security of the decoy state method for quantum key distribution

Quantum cryptography or, more precisely, quantum key distribution (QKD), is one of the advanced areas in the field of quantum technologies. The confidentiality of keys distributed with the use of QKD protocols is guaranteed by the fundamental laws of quantum mechanics. This paper is devoted to the decoy state method, a countermeasure against vulnerabilities caused by the use of coherent states of light for QKD protocols whose security is proved under the assumption of single-photon states. We give a formal security proof of the decoy state method against all possible attacks. We compare two widely known attacks on multiphoton pulses: photon-number splitting and beam splitting. Finally, we discuss the equivalence of polarization and phase coding.
Designing for the future in the age of pandemics: a future-ready design research (FRDR) process

The recent pandemic has painfully reminded us of the need for IS design to be informed by possible futures and conscious of undesirable futures – Within months, many of the nice-to-have IS functionalities have become must-haves; Technology solutions in response to the pandemic have raised privacy and equality concerns. Although design science research fundamentally focuses on shaping artefacts and events to create a more desirable future, there has been limited guidance on how futures should be accounted for. This article addresses the gap by integrating insights from future-oriented IS research and futures research to develop guidelines for engaging with futures throughout the design science research process. The future-ready design research (FRDR) process prompts researchers to be more aware of futures, to foster the innovative foresight for actively pursuing the preferred future, and to espouse the responsible foresight for consciously avoiding undesirable futures. The guidelines are illustrated with a design science research project on outbreak analytics and the instantiated system's subsequent adaptation and utilisation in COVID-19.
Parallel and scalable Dunn Index for the validation of big data clusters

Parallelizing data clustering algorithms has attracted the interest of many researchers over the past few years. Many efficient parallel algorithms were proposed to build partitioning over a huge volume of data. The effectiveness of these algorithms is attributed to the distribution of data among a cluster of nodes and to the parallel computation models. Although the effectiveness of parallel models to deal with increasing volume of data little work is done on the validation of big clusters. To deal with this issue, we propose a parallel and scalable model, referred to as S-DI (Scalable Dunn Index), to compute the Dunn Index measure for an internal validation of clustering results. Rather than computing the Dunn Index on a single machine in the clustering validation process, the new proposed measure is computed by distributing the partitioning among a cluster of nodes using a customized parallel model under Apache Spark framework. The proposed S-DI is also enhanced by a Sketch and Validate sampling technique which aims to approximate the Dunn Index value by using a small representative data-sample. Different experiments on simulated and real datasets showed a good scalability of our proposed measure and a reliable validation compared to other existing measures when handling large scale data.
Development of An e-commerce Sales Chatbot

This paper presents the development of an e-commerce sales chatbot in order to provide customer support and increase sales. The system uses machine learning for natural language understanding. It is developed on an modular chatbot framework. The system has several components. First, a web based natural language training platform. Second, a microservice to classify input text and extract entities. Finally, a framework which routes user request to specific controller for processing and serves the response.
Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.
AINet: Association Implantation for Superpixel Segmentation

Recently, some approaches are proposed to harness deep convolutional networks to facilitate superpixel segmentation. The common practice is to first evenly divide the image into a pre-defined number of grids and then learn to associate each pixel with its surrounding grids. However, simply applying a series of convolution operations with limited receptive fields can only implicitly perceive the relations between the pixel and its surrounding grids. Consequently, existing methods often fail to provide an effective context when inferring the association map. To remedy this issue, we propose a novel Association Implantation (AI) module to enable the network to explicitly capture the relations between the pixel and its surrounding grids. The proposed AI module directly implants the grid features to the surrounding of its corresponding central pixel, and conducts convolution on the padded window to adaptively transfer knowledge between them. With such an implantation operation, the network could explicitly harvest the pixel-grid level context, which is more in line with the target of superpixel segmentation comparing to the pixelwise relation. Furthermore, to pursue better boundary precision, we design a boundary-perceiving loss to help the network discriminate the pixels around boundaries in hidden feature level, which could benefit the subsequent inferring modules to accurately identify more boundary pixels. Extensive experiments on BSDS500 and NYUv2 datasets show that our method could achieve state-of-the-art performance. Code and pre-trained model are available at https://github.com/wangyxxjtu/AINet-ICCV2021.
Student privacy issues in online learning environments

This study explored student privacy issues in online learning environments. Vignettes are used to present the various privacy scenarios occurred in online learning environment. The study showed that there are direct legal ramifications of concern to students, as a result of sharing individual grades among group members and providing public feedback in blogs. Other privacy issues occurred in more nuanced ways, such as open access to each other’s work, transparent reflections, public comments, critical analysis of the assignments, and collaborative evaluations of students’ work. Suggestions are provided to address these privacy-related issues, including focusing on students’ products, not students’ names; informing students of the benefits they will receive and the contributions they will make by sharing their work publicly; and options for students to keep their identity private when they feel uncomfortable about sharing their work publicly.
Zero-shot action recognition in videos: A survey

Zero-shot action recognition has attracted attention in the last years and many approaches have been proposed for recognition of objects, events and actions in images and videos. There is a demand for methods that can classify instances from classes that are not present in the training of models, especially in the complex problem of automatic video understanding, since collecting, annotating and labeling videos are difficult and laborious tasks. We have identified that there are many methods available in the literature, however, it is difficult to categorize which techniques can be considered state of the art. Despite the existence of some surveys about zero-shot action recognition in still images and experimental protocol, there is no work focused on videos. Therefore, we present a survey of the methods that comprise techniques to perform visual feature extraction and semantic feature extraction as well to learn the mapping between these features considering specifically zero-shot action recognition in videos. We also provide a complete description of datasets, experiments and protocols, presenting open issues and directions for future work, essential for the development of the computer vision research field.
RCNet: Incorporating Structural Information Into Deep RNN for Online MIMO-OFDM Symbol Detection With Limited Training

In this paper, we investigate online learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) - reservoir computing (RC). We first introduce the Time-Frequency RC to take advantage of the structural information inherent in OFDM signals. Using the time domain RC and the time-frequency RC as building blocks, we provide two extensions of the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking multiple time-frequency RCs into a deep structure. The combination of RNN dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep network enables RCNet to handle the interference and nonlinear distortion of MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based detection strategies, RCNet is also shown to provide a good generalization performance even with a limited online training set (i.e, similar amount of reference signals/training as standard model-based approaches). Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due to power amplifier compression in the transmitter or due to finite quantization resolution in the receiver.
AirID: Injecting a Custom RF Fingerprint for Enhanced UAV Identification using Deep Learning

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting `signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in `train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> for the evaluated cases.
Trade openness, financial development and economic growth in North African countries

Abstract This contribution investigates the relationships between financial development, trade openness and economic growth on a panel of four North African countries (Tunisia, Morocco, Algeria and Egypt), over a 5‐year period from 1991 to 2015. In fact, using the generalized method of moment (GMM) in a panel data analysis, we found strong evidence of a positive link between trade openness and economic growth. Moreover, we found that trade openness appears to be working as a complement to financial development and that its effect is more pronounced in the presence of the financial development variable. Therefore, the policy implications of this study appeared clear. Hence, improvement efforts need to be driven by local‐level reforms to ensure the development of domestic financial system and take full advantage of the technology transfer induced by international trade.
500-Fps Omnidirectional Visual Tracking Using Three-Axis Active Vision System

This study proposes a visual tracking system with 500-fps image processing that can detect and track a fast-moving appearance-varying target in a 360-degree-field of view (FOV) from high-spatial-resolution images. The system consists of a high-speed camera, a pan-tilt Galvano-mirror system, and a mirror-drive high-speed pan control system with 360-degree-rotational capacity. Omnidirectional visual tracking of a remote object using complex visual features becomes possible due to the acquisition of high-spatial-resolution images of the target object via the system. We introduce an intelligent high frame rate (HFR) target-tracking algorithm that can simultaneously detect and track an appearance-varying object against a complex background for high-speed visual feedback control. Furthermore, the proposed surveillance system enables robust visual tracking under low-light conditions using a 3-D light detection and ranging (LiDAR) sensor system and a simultaneously controlled active spotlight projection system. The effectiveness of the proposed omnidirectional visual tracking system was demonstrated by conducting several omnidirectional detecting and tracking experiments on persons dozens of meters away using a 25-mm focal length lens under both daytime and night-time conditions.
Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Meta-learning, or "learning to learn", refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that uses either separate within-task training and test sets, like MAML, or joint within-task training and test sets, like Reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed, under given technical conditions, for the two classes via novel Individual Task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.