Reviewerly/exHarmony huggingface dataset:
Universal nonadiabatic geometric gates protected by dynamical decoupling

Dynamical decoupling provides an effective method to protect nonadiabatic geometric quantum computation against environment-induced decoherence. Although some dynamical-decoupling-protected nonadiabatic geometric gates were proposed, they need to combine some other dynamical gates to perform universal quantum computation. In this paper we propose a scheme to realize a universal set of dynamical-decoupling-protected nonadiabatic geometric gates, which not only possesses the resilience to environment-induced decoherence but also allows us to perform universal quantum computation all by geometric operations.
Toward Decentralized Fair Data Trading Based on Blockchain

Various emerging applications and services such as smartphones, wearable devices, and Inter-net of Things (IoT) have brought great convenience to people's daily life, while producing huge amounts of data. Mass data have become a valuable asset, which creates a new business pattern called data trading. However, fairness becomes a challenge when conducting online data trading between participants who are not fully trusted by each other. That is, if a data seller sends the data before being paid, a data buyer might obtain the data without paying, and conversely if a data buyer pays before receiving the data, the data seller might not send the data contractually. Traditionally, a trusted third party is usually employed to settle the disputes between buyers and sellers, but this centralized party is vulnerable to the single-point-of-failure issue. Fortunately, Blockchain provides an approach to realizing data trading without centralized trusted third parties. In this article, we introduce Fairtrade, a decentralized fair data trading framework, to solve the challenges of data availability and trading fairness in decentralized data trading. We propose two different models with potential instantiations. In the first solution, we take advantage of homomorphic encryption and data sample techniques to improve the reliability of the system, and further guarantee the availability of the data during data trading. In the second solution, we integrate double-authentication-preventing signatures with smart contracts to achieve fairness during data trading. We also evaluate Fairtrade by implementing both frameworks, in which we test the time consumption of the main algorithms and also test the gas cost of the functions in the smart contracts on the blockchain. The experimental results show the practicality of the proposal.
MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net

Accurate hepatic vessel segmentation and registration using ultrasound (US) can contribute to beneficial navigation during hepatic surgery. However, it is challenging due to noise and speckle in US imaging and liver deformation. Therefore, a workflow is developed using a reduced 3D U-Net for segmentation, followed by non-rigid coherent point drift (CPD) registration. By means of electromagnetically tracked US, 61 3D volumes were acquired during surgery. Dice scores of 0.77, 0.65 and 0.66 were achieved for segmentation of all vasculature, hepatic vein and portal vein respectively. This compares to inter-observer variabilities of 0.85, 0.88 and 0.74 respectively. Target registration error at a tumor lesion of interest was lower (7.1 mm) when registration is performed either on the hepatic or the portal vein, compared to using all vasculature (8.9 mm). Using clinical data, we developed a workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery.
High-Throughput Lossy-to-Lossless 3D Image Compression

The rapid increase in medical and biomedical image acquisition rates has opened up new avenues for image analysis, but has also introduced formidable challenges. This is evident, for example, in selective plane illumination microscopy where acquisition rates of about 1-4 GB/s sustained over several days have redefined the scale of I/O bandwidth required by image analysis tools. Although the effective bandwidth could, principally, be increased by lossy-to-lossless data compression, this is of limited value in practice due to the high computational demand of current schemes such as JPEG2000 that reach compression throughput of one order of magnitude below that of image acquisition. Here we present a novel lossy-to-lossless data compression scheme with a compression throughput well above 4 GB/s and compression rates and rate-distortion curves competitive with those achieved by JPEG2000 and JP3D.
A Wearable Fall Detection System Based on Body Area Networks

Falls can have serious consequences for people, leading to restrictions in mobility or, in the worst case, to traumatic-based cases of death.To provide rapid assistance, a portable fall detection system has been developed that is capable of detecting fall situations and, if necessary, alerting emergency services without any user interaction.The prototype is designed to facilitate reliable fall detection and to classify several fall types and human activities.This solution represents a life-saving service for every person that will significantly improve assistance in the case of fall events, which are a part of daily life.Additionally, this approach facilitates independent system operation, since the system does not depend on sensor or network units located within a building structure.This article also introduces fall analysis.To guarantee functional safety, a hazard analysis method named system-theoretic accident model and processes (STAMP) is applied.
Beyond QoE: Diversity Adaptation in Video Streaming at the Edge

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, $3G$ HSDPA data set from the Internet, as well as the $4G$ /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.
WITHDRAWN: Implementing lossless compression during image processing by integrated approach

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Multi-source Open-Set Deep Adversarial Domain Adaptation

We introduce a novel learning paradigm of multi-source open-set unsupervised domain adaptation (MS-OSDA). Recently, the notion of single-source open-set domain adaptation (SS-OSDA) which considers the presence of previously unseen open-set (unknown) classes in the target-domain in addition to the source-domain closed-set (known) classes has drawn attention. In the SS-OSDA setting, the labeled samples are assumed to be drawn from the same source. Yet, it is more plausible to assume that the labeled samples are distributed over multiple source-domains, but the existing SS-OSDA techniques cannot directly handle this more realistic scenario considering the diversities among multiple source-domains. As a remedy, we propose a novel adversarial learning-driven approach to deal with MS-OSDA. Precisely, we model a shared feature space for all the domains which explicitly mitigates the domain-gap among the source-domains. The adversarial learning strategy is introduced to align the known-class samples from the target-domain with the source data while making the unknown-classes more separable. We validate our method on the Office-31, Office-Home, Office-CalTech, and Digits datasets and find that the proposed model consistently outperforms the baseline and benchmark SS-OSDA approaches.
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach

Given a document and a target aspect (e.g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect.Previous studies usually assume a small pre-defined set of aspects and fall short of summarizing on other diverse topics.In this work, we study summarizing on arbitrary aspects relevant to the document, which significantly expands the application of the task in practice.Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme, both of which integrate rich external knowledge sources such as Concept-Net and Wikipedia.Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents given pre-defined or arbitrary aspects. 1
Matérn Gaussian Processes on Riemannian Manifolds

Gaussian processes are an effective model class for learning unknown functions,particularly in settings where accurately representing predictive uncertainty is ofkey importance. Motivated by applications in the physical sciences, the widelyused Matern class of Gaussian processes has recently been generalized to model ´functions whose domains are Riemannian manifolds, by re-expressing said processes as solutions of stochastic partial differential equations. In this work, wepropose techniques for computing the kernels of these processes via spectral theoryof the Laplace–Beltrami operator in a fully constructive manner, thereby allowingthem to be trained via standard scalable techniques such as inducing point methods.We also extend the generalization from the Matern to the widely-used squared ´exponential Gaussian process. By allowing Riemannian Matern Gaussian pro- ´cesses to be trained using well-understood techniques, our work enables their usein mini-batch, online, and non-conjugate settings, and makes them more accessibleto machine learning practitioners.
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems

The lack of time-efficient and reliable evaluation methods hamper the development of conversational dialogue systems (chatbots).Evaluations requiring humans to converse with chatbots are time and cost-intensive, put high cognitive demands on the human judges, and yield low-quality results.In this work, we introduce Spot The Bot, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots.Human judges then only annotate for each entity in a conversation whether they think it is human or not (assuming there are humans participants in these conversations).These annotations then allow us to rank chatbots regarding their ability to mimic the conversational behavior of humans.Since we expect that all bots are eventually recognized as such, we incorporate a metric that measures which chatbot can uphold human-like behavior the longest, i.e., Survival Analysis.This metric has the ability to correlate a bot's performance to certain of its characteristics (e.g., fluency or sensibleness), yielding interpretable results.The comparably low cost of our framework allows for frequent evaluations of chatbots during their evaluation cycle.We empirically validate our claims by applying Spot The Bot to three domains, evaluating several stateof-the-art chatbots, and drawing comparisons to related work.The framework is released as a ready-to-use tool.
Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow

Abstract. Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking, processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysis of long-term, radiolarian-based palaeoclimatic records from siliceous-remnant-bearing samples.
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers

For safety-critical applications such as autonomous driving, CNNs have to be robust with respect to unavoidable image corruptions, such as image noise. While previous works addressed the task of robust prediction in the context of full-image classification, we consider it for dense semantic segmentation. We build upon an insight from image classification that output robustness can be improved by increasing the network-bias towards object shapes. We present a new training schema that increases this shape bias. Our basic idea is to alpha-blend a portion of the RGB training images with faked images, where each class-label is given a fixed, randomly chosen color that is not likely to appear in real imagery. This forces the network to rely more strongly on shape cues. We call this data augmentation technique “Painting-by-Numbers”. We demonstrate the effectiveness of our training schema for DeepLabv3 $$+$$ with various network backbones, MobileNet-V2, ResNets, and Xception, and evaluate it on the Cityscapes dataset. With respect to our 16 different types of image corruptions and 5 different network backbones, we are in 74% better than training with clean data. For cases where we are worse than a model trained without our training schema, it is mostly only marginally worse. However, for some image corruptions such as images with noise, we see a considerable performance gain of up to 25%.
Three-Stream Cross-Modal Feature Aggregation Network for Light Field Salient Object Detection

Light field saliency detection can leverage the rich visual features of light field(LF) to highlight the salient regions, but existing CNN-based saliency detection methods are specifically designed for RGB image, not for light field. To tackle this problem, a three-stream cross-modal feature aggregation network is proposed for 4D light field saliency detection. To fully utilize the rich visual features of light field, three sub-networks are set up to analyse focal stack, all-focus image, and depth map respectively. Then, feature aggregation modules are used to aggregate cross-level features in a top-down manner. Finally, a cross-modal feature fusion module is designed to fuse the aggregated features of various modalities from the three sub-networks, which can identify salient object quickly and precisely. Extensive experiments on three benchmark datasets show that the effectiveness and superiority of the proposed algorithm qualitatively and quantitatively on five evaluation metrics compared with state-of-the-art(SOTA) methods.
Imbalanced learning: Improving classification of diabetic neuropathy from magnetic resonance imaging

One of the fundamental challenges when dealing with medical imaging datasets is class imbalance. Class imbalance happens where an instance in the class of interest is relatively low, when compared to the rest of the data. This study aims to apply oversampling strategies in an attempt to balance the classes and improve classification performance. We evaluated four different classifiers from k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP) and decision trees (DT) with 73 oversampling strategies. In this work, we used imbalanced learning oversampling techniques to improve classification in datasets that are distinctively sparser and clustered. This work reports the best oversampling and classifier combinations and concludes that the usage of oversampling methods always outperforms no oversampling strategies hence improving the classification results.
Universal Nonadiabatic Control of Small-Gap Superconducting Qubits

A new method for controlling certain quantum bits offers fast operation of a novel composite qubit that boosts coherence times and immunity to noise, all of which could lead to improved quantum computing performance.
Multicenter assessment of augmented reality registration methods for image-guided interventions

To evaluate manual and automatic registration times and registration accuracies on HoloLens 2 for aligning a 3D CT phantom model onto a CT grid, a crucial step for intuitive 3D navigation during CT-guided interventions; to compare registration times between HoloLens 1 and 2. Eighteen participants in various stages of clinical training across two academic centers performed registration of a 3D CT phantom model onto a CT grid using HoloLens 2. Registration times and accuracies were compared among different registration methods, clinical experience levels, and consecutive attempts. Registration times were also compared retrospectively to prior HoloLens 1 results. Mean aggregate manual registration times were 27.7 s, 24.3 s, and 72.8 s for one-handed gesture, two-handed gesture, and Xbox controller, respectively; mean automatic registration time was 5.3 s (ANOVA p < 0.0001). No significant difference in registration times was found among attendings, residents and fellows, and medical students (p > 0.05). Significant improvements in registration times were detected across consecutive attempts using hand gestures (p < 0.01). Compared to prior HoloLens 1 data, hand gesture registration was 81.7% faster with HoloLens 2 (p < 0.05). Registration accuracies were not significantly different across manual registration methods, measuring at 5.9 mm, 9.5 mm, and 8.6 mm with one-handed gesture, two-handed gesture, and Xbox controller, respectively (p > 0.05). Manual registration times decreased significantly on HoloLens 2, approaching those of automatic registration and outperforming Xbox controller registration. Fast, adaptive, and accurate registration of holographic models of cross-sectional imaging is paramount for the implementation of augmented reality-assisted 3D navigation during CT-guided interventions.
Privacy Care

The emergence of ubiquitous computing (UbiComp) environments has increased the risk of undesired access to individuals’ physical space or their information, anytime and anywhere, raising potentially serious privacy concerns. Individuals lack awareness and control of the vulnerabilities in everyday contexts and need support and care in regulating disclosures to their physical and digital selves. Existing GUI-based solutions, however, often feel physically interruptive, socially disruptive, time-consuming and cumbersome. To address such challenges, we investigate the user interaction experience and discuss the need for more tangible and embodied interactions for effective and seamless natural privacy management in everyday UbiComp settings. We propose the Privacy Care interaction framework, which is rooted in the literature of privacy management and tangible computing. Keeping users at the center, Awareness and Control are established as the core parts of our framework. This is supported with three interrelated interaction tenets: Direct, Ready-to-Hand, and Contextual . Direct refers to intuitiveness through metaphor usage. Ready-to-Hand supports granularity, non-intrusiveness, and ad hoc management, through periphery-to-center style attention transitions. Contextual supports customization through modularity and configurability. Together, they aim to provide experience of an embodied privacy care with varied interactions that are calming and yet actively empowering. The framework provides designers of such care with a basis to refer to, to generate effective tangible tools for privacy management in everyday settings. Through five semi-structured focus groups, we explore the privacy challenges faced by a sample set of 15 older adults (aged 60+) across their cyber-physical-social spaces. The results show conformity to our framework, demonstrating the relevance of the facets of the framework to the design of privacy management tools in everyday UbiComp contexts.