Reviewerly/exHarmony huggingface dataset:
Tokens Shuffling Approach for Privacy, Security, and Reliability in IoHT under a Pandemic

Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data’s privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system’s performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services.
Qubit Mapping for Reconfigurable Atom Arrays

Because of the largest number of qubits available, and the massive parallel execution of entangling two-qubit gates, atom arrays is a promising platform for quantum computing. The qubits are selectively loaded into arrays of optical traps, some of which can be moved during the computation itself. By adjusting the locations of the traps and shining a specific global laser, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. In comparison, previous QC architectures only generate entanglement on a fixed set of quantum register pairs. Thus, reconfigurable atom arrays (RAA) present a new challenge for QC compilation, especially the qubit mapping/layout synthesis stage which decides the qubit placement and gate scheduling. In this paper, we consider an RAA QC architecture that contains multiple arrays, supports 2D array movements, represents cutting-edge experimental platforms, and is much more general than previous works. We start by systematically examining the fundamental constraints on RAA imposed by physics. Built upon this understanding, we discretize the state space of the architecture, and we formulate layout synthesis for such an architecture to a satisfactory modulo theories problem. Finally, we demonstrate our work by compiling the quantum approximate optimization algorithm (QAOA), one of the promising near-term quantum computing applications. Our layout synthesizer reduces the number of required native two-qubit gates in 22-qubit QAOA by 5.72x (geomean) compared to leading experiments on a superconducting architecture. Combined with a better coherence time, there is an order-of-magnitude increase in circuit fidelity.
Reliable Machine Learning for Wearable Activity Monitoring

Wearable devices are becoming popular for health and activity monitoring. The machine learning (ML) models for these applications are trained by collecting data in a laboratory with precise control of experimental settings. However, during real-world deployment/usage, the experimental settings (e.g., sensor position or sampling rate) may deviate from those used during training. This discrepancy can degrade the accuracy and effectiveness of the health monitoring applications. Therefore, there is a great need to develop reliable ML approaches that provide high accuracy for real-world deployment. In this paper, we propose a novel statistical optimization approach referred as StatOpt that automatically accounts for the real-world disturbances in sensing data to improve the reliability of ML models for wearable devices. We theoretically derive the upper bounds on sensor data disturbance for StatOpt to produce a ML model with reliability certificates. We validate StatOpt on two publicly available datasets for human activity recognition. Our results show that compared to standard ML algorithms, the reliable ML classifiers enabled by the StatOpt approach improve the accuracy up to 50% in real-world settings with zero overhead, while baseline approaches incur significant overhead and fail to achieve comparable accuracy.
Arjun

Given a Boolean formula ϕ over the set of variables X and a projection set P ⊆ X, then if I ⊆ P is independent support of P, then if two solutions of ϕ agree on I, then they also agree on P. The notion of independent support is related to the classical notion of definability dating back to 1901, and have been studied over the decades. Recently, the computational problem of determining independent support for a given formula has attained importance owing to the crucial importance of independent support for hashing-based counting and sampling techniques.
Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN

In this paper, we present a medical AttentIon Denoising Super Resolution Generative Adversarial Network (AID-SRGAN) for diographic image super-resolution. First, we present a medical practical degradation model that considers various degradation factors beyond downsampling. To the best of our knowledge, this is the first composite degradation model proposed for radiographic images. Furthermore, we propose AID-SRGAN, which can simultaneously denoise and generate high-resolution (HR) radiographs. In this model, we introduce an attention mechanism into the denoising module to make it more robust to complicated degradation. Finally, the SR module reconstructs the HR radiographs using the "clean" low-resolution (LR) radiographs. In addition, we propose a separate-joint training approach to train the model, and extensive experiments are conducted to show that the proposed method is superior to its counterparts. e.g., our proposed method achieves $31.90$ of PSNR with a scale factor of $4 \times$, which is $7.05 \%$ higher than that obtained by recent work, SPSR [16]. Our dataset and code will be made available at: https://github.com/yongsongH/AIDSRGAN-MICCAI2022.
Cross-domain semantic alignment: concrete concepts are more abstract than you think

We can easily evaluate similarities between concepts within semantic domains, e.g. doctor and nurse, or violin and piano. Here, we show that people are also able to evaluate similarities across domains, e.g. aligning doctors with pianos and nurses with violins. We argue that understanding how people do this is important for understanding conceptual organization and the ubiquity of metaphorical language. We asked people to answer questions of the form 'If a nurse were an animal, they would be a(n) …' (Experiments 1 and 2) and asked them to explain the basis for their response (Experiment 1). People converged to a surprising degree (e.g. 20% answered 'cat'). In Experiment 3, we presented people with cross-domain mappings of the form 'If a nurse were an animal, they would be a cat' and asked them to indicate how good each mapping was. The results showed that the targets people chose and their goodness ratings of a given response were predicted by similarity along abstract semantic dimensions such as valence, speed and genderedness. Reliance on such dimensions was also the most common explanation for their responses. Altogether, we show that people can evaluate similarity between very different domains in predictable ways, suggesting either that seemingly concrete concepts are represented along relatively abstract dimensions (e.g. weak-strong) or that they can be readily projected onto these dimensions. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.
MoreThanSentiments: A text analysis package

<h2>Abstract</h2> Text mining on a large corpus of data has gained utility and popularity over recent years owing to advancements in information retrieval and machine learning methods. However, popular text mining software packages mainly focus on either sentiment analysis or semantic meaning extraction, requiring pretraining on a large corpus of text data. In comparison, MoreThanSentiments provides computation of newer text attribution measures, including boiler score, specificity, redundancy, and hard info, which have been proposed in accounting analytics literature. Our software package, available in Python, is flexible in terms of parameter setting and is adaptable to different applications. Through this package, we seek to simplify the process of deploying nontrivial information extraction techniques published in domain-specific text analysis research into domain-agnostic analytics applications.
Cascaded transformer U-net for image restoration

Image restoration is one of the most important computer vision tasks, aiming at recovering high-quality images from degraded or low-quality observations. The restoration methods based on convolutional neural networks (CNNs) have achieved attractive performance, however, as convolutions only intake local information, CNN-based methods have limitations in modeling objects in long ranges and extracting global information. In addition, existing one-stage methods damage the performance due to lacking diversified receptive fields. In this paper, we propose a multi-stage cascaded transformer architecture for image restoration. Firstly, the Swin transformer based encoder relying on self-attention is used to improve the modeling ability for long-range objects and outputs hierarchical multi-level semantic features. Then, a shape perceiving module is designed and embedded in the decoder to enhance the representation of irregular objects, Moreover, a multi-stage cascaded encoder-decoder architecture possessing diversified receptive fields is proposed to progressively obtain fine restoration results and thus boost the performance. We conduct extensive experiments, including image deraining, underwater image enhancement, near infrared image colorization and low-light image enhancement. The results show that our proposed method can achieve comparable or better performance than state-of-the-art methods while with less training and inference costs.
Uncertainty-aware visual analytics: scope, opportunities, and challenges

Abstract In many applications, visual analytics (VA) has developed into a standard tool to ease data access and knowledge generation. VA describes a holistic cycle transforming data into hypothesis and visualization to generate insights that enhance the data. Unfortunately, many data sources used in the VA process are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process but does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization, defined as uncertainty-aware visual analytics (UAVA). Here, a recap of uncertainty definition and description is used as a starting point to insert novel components in the visual analytics cycle. These components assist in capturing uncertainty throughout the VA cycle. Further, different data types, hypothesis generation approaches, and uncertainty-aware visualization approaches are discussed that fit in the defined UAVA cycle. In addition, application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of UAVA are provided.
Knowledge Management and Sustainability Performance of Hospital Organisations: The Healthcare Managers’ Perspective

In a changing context, the use of knowledge management (KM) generates value and a long-lasting competitive advantage. As a critical issue for societal development, sustainable organisations must consider the relationship between KM and healthcare. They must investigate how knowledge is created and identify potential predictors of knowledge-sharing behaviour to support a hospital’s long-term knowledge-management strategy. Managers could benefit from KM strategies to improve the performance of hospitals and other healthcare organisations. This study aimed to determine the knowledge management and sustainability performance of a hospital organisation based on the perspective of the healthcare managers. A cross-sectional research approach was employed involving the healthcare managers of a government-subsidised hospital in the Hail Region of Saudi Arabia. The study was conducted between September and October 2022. The results demonstrated that knowledge management was commendable (4.41 ± 0.470) and had high sustainability performance (4.28 ± 0.558). Predictors for knowledge management included gender (B = 0.201; p = 0.002), managerial position (B = 0.285; p &lt; 0.000), years of experiences as manager (B = 0.319; p &lt; 0.000) and educational attainment (B = 0.092; p = 0.003). The predictors for sustainability were gender (B = 0.023; p &lt; 0.000), managerial position (B = 0.352; p &lt; 0.000) and years of experience as a manager (B = 0.0648; p &lt; 0000). The study found that knowledge management was a predictive factor in sustainability regarding economic (B = 0.735; p &lt; 0.000), social (B = 1.028; p &lt; 0.000), environmental (B = 0.774; p &lt; 0.000), technical (B = 0.751; p &lt; 0.000) and governance (B = 0.526; p &lt; 0.000), while knowledge management had a strong correlation to sustainability performance (R = 0.663; p &lt; 0.000). Therefore, knowledge management for learning should be consistently created and shared to keep members involved in the core of operations. When knowledge is put into practice, it can help an organisation innovate, sustain better performance and guarantee long-term success. These study findings may additionally persuade workers to put more effort into knowledge management.
Multi-Class Breast Cancer Histopathological Image Classification Using Multi-Scale Pooled Image Feature Representation (MPIFR) and One-Versus-One Support Vector Machines

Breast cancer (BC) is currently the most common form of cancer diagnosed worldwide with an incidence estimated at 2.26 million in 2020. Additionally, BC is the leading cause of cancer death. Many subtypes of breast cancer exist with distinct biological features and which respond differently to various treatment modalities and have different clinical outcomes. To ensure that sufferers receive lifesaving patients-tailored treatment early, it is crucial to accurately distinguish dangerous malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) subtypes of tumors from adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma benign harmless subtypes. An excellent automated method for detecting malignant subtypes of tumors is desirable since doctors do not identify 10% to 30% of breast cancers during regular examinations. While several computerized methods for breast cancer classification have been proposed, deep convolutional neural networks (DCNNs) have demonstrated superior performance. In this work, we proposed an ensemble of four variants of DCNNs combined with the support vector machines classifier to classify breast cancer histopathological images into eight subtypes classes: four benign and four malignant. The proposed method utilizes the power of DCNNs to extract highly predictive multi-scale pooled image feature representation (MPIFR) from four resolutions (40×, 100×, 200×, and 400×) of BC images that are then classified using SVM. Eight pre-trained DCNN architectures (Inceptionv3, InceptionResNetv2, ResNet18, ResNet50, DenseNet201, EfficientNetb0, shuffleNet, and SqueezeNet) were individually trained and an ensemble of the four best-performing models (ResNet50, ResNet18, DenseNet201, and EfficientNetb0) was utilized for feature extraction. One-versus-one SVM classification was then utilized to model an 8-class breast cancer image classifier. Our work is novel because while some prior work has utilized CNNs for 2- and 4-class breast cancer classification, only one other prior work proposed a solution for 8-class BC histopathological image classification. A 6B-Net deep CNN model was utilized, achieving an accuracy of 90% for 8-class BC classification. In rigorous evaluation, the proposed MPIFR method achieved an average accuracy of 97.77%, with 97.48% sensitivity, and 98.45% precision on the BreakHis histopathological BC image dataset, outperforming the prior state-of-the-art for histopathological breast cancer multi-class classification and a comprehensive set of DCNN baseline models.
Hong-Ou-Mandel Interference between Two Hyperentangled Photons Enables Observation of Symmetric and Antisymmetric Particle Exchange Phases

Two-photon Hong-Ou-Mandel (HOM) interference is a fundamental quantum effect with no classical counterpart. The exiting researches on two-photon interference were mainly limited in one degree of freedom (DoF), hence it is still a challenge to realize the quantum interference in multiple DoFs. Here we demonstrate the HOM interference between two hyper-entangled photons in two DoFs of polarization and orbital angular momentum (OAM) for all the sixteen hyper-entangled Bell states. We observe hyper-entangled two-photon interference with bunching effect for ten symmetric states (nine Boson-Boson states, one Fermion-Fermion state) and anti-bunching effect for six anti-symmetric states (three Boson-Fermion states, three Fermion-Boson states). More interestingly, expanding the Hilbert space by introducing an extra DoF for two photons enables to transfer the unmeasurable external phase in the initial DoF to a measurable internal phase in the expanded two DoFs. We directly measured the symmetric exchange phases being $0.012 \pm 0.002$, $0.025 \pm 0.002$ and $0.027 \pm 0.002$ in radian for the three Boson states in OAM and the anti-symmetric exchange phase being $0.991 \pi \pm 0.002$ in radian for the other Fermion state, as theoretical predictions. Our work may not only pave the way for more wide applications of quantum interference, but also develop new technologies by expanding Hilbert space in more DoFs.
FFP: A Force Field Protocol for the tactical management of UAV conflicts

In recent years, we have seen a tremendous growth in the adoption of Unmanned Aerial Vehicles (UAVs). Nowadays, UAVs are used in many different industries such as agriculture, inspection (bridges, pipelines, etc.), parcel delivery, etc. In the near future, this will lead to a substantial increase of aircraft in our airspace, especially in urban areas. Many existing collision avoidance approaches rely on heavy and/or expensive sensors, which limits its use for real UAVs due to increased costs, weight and complexity. Hence, to address this problem, in this paper we present a solution for the tactical management (i.e. in-flight) of UAV conflicts outdoors that introduces minimal requirements: a wireless interface and a GPS module. Specifically, we provide a collision avoidance algorithm based on artificial potential fields to provide flight safety. Our solution, called Force Field Protocol (FFP), allows the UAVs to autonomously detect each other using wireless communications, and to maintain a safe distance between them without the intervention of any central service. Experiments performed in our multi-UAV simulator ArduSim show that, with our approach, collisions between two UAVs are completely avoided in a wide set of scenarios, while introducing low disturbances to the original flight plans. Specifically, in the scenarios that we tested, the additional flight time introduced will be only 7 s longer in the worst case; in addition, it is able to improve upon previous approaches by reducing flight time by up to 54 s. We have shown experimentally that our approach can be scaled easily up to 100 UAVs, and that the probability of a collision is very low (< 0.06) despite flying in a small area (2.5 km × 2.5 km).
A combination of ridge and Liu regressions for extreme learning machine

Extreme learning machine (ELM) as a type of feedforward neural network has been widely used to obtain beneficial insights from various disciplines and real-world applications. Despite the advantages like speed and highly adaptability, instability drawbacks arise in case of multicollinearity, and to overcome this, additional improvements were needed. Regularization is one of the best choices to overcome these drawbacks. Although ridge and Liu regressions have been considered and seemed effective regularization methods on ELM algorithm, each one has own characteristic features such as the form of tuning parameter, the level of shrinkage or the norm of coefficients. Instead of focusing on one of these regularization methods, we propose a combination of ridge and Liu regressions in a unified form for the context of ELM as a remedy to aforementioned drawbacks. To investigate the performance of the proposed algorithm, comprehensive comparisons have been carried out by using various real-world data sets. Based on the results, it is obtained that the proposed algorithm is more effective than the ELM and its variants based on ridge and Liu regressions, RR-ELM and Liu-ELM, in terms of the capability of generalization. Generalization performance of proposed algorithm on ELM is remarkable when compared to RR-ELM and Liu-ELM, and the generalization performance of the proposed algorithm on ELM increases as the number of nodes increases. The proposed algorithm outperforms ELM in all data sets and all node numbers in that it has a smaller norm and standard deviation of the norm. Additionally, it should be noted that the proposed algorithm can be applied for both regression and classification problems.
Adaptive resource optimization for edge inference with goal-oriented communications

Abstract Goal-oriented communications represent an emerging paradigm for efficient and reliable learning at the wireless edge, where only the information relevant for the specific learning task is transmitted to perform inference and/or training. The aim of this paper is to introduce a novel system design and algorithmic framework to enable goal-oriented communications. Specifically, inspired by the information bottleneck principle and targeting an image classification task, we dynamically change the size of the data to be transmitted by exploiting banks of convolutional encoders at the device in order to extract meaningful and parsimonious data features in a totally adaptive and goal-oriented fashion. Exploiting knowledge of the system conditions, such as the channel state and the computation load, such features are dynamically transmitted to an edge server that takes the final decision, based on a proper convolutional classifier. Hinging on Lyapunov stochastic optimization, we devise a novel algorithmic framework that dynamically and jointly optimizes communication, computation, and the convolutional encoder classifier, in order to strike a desired trade-off between energy, latency, and accuracy of the edge learning task. Several simulation results illustrate the effectiveness of the proposed strategy for edge learning with goal-oriented communications.