Reviewerly/exHarmony huggingface dataset:
Membership Inference Attacks and Generalization

Membership inference (MI) attacks highlight a privacy weakness in present stochastic training methods for neural networks. It is not well understood, however, why they arise. Are they a natural consequence of imperfect generalization only? Which underlying causes should we address during training to mitigate these attacks? Towards answering such questions, we propose the first approach to explain MI attacks and their connection to generalization based on principled causal reasoning. We offer causal graphs that quantitatively explain the observed MI attack performance achieved for 6 attack variants. We refute several prior non-quantitative hypotheses that over-simplify or over-estimate the influence of underlying causes, thereby failing to capture the complex interplay between several factors. Our causal models also show a new connection between generalization and MI attacks via their shared causal factors. Our causal models have high predictive power (0.90), i.e., their analytical predictions match with observations in unseen experiments often, which makes analysis via them a pragmatic alternative.
Spin Entropy

Two types of randomness are associated with a mixed quantum state: the uncertainty in the probability coefficients of the constituent pure states and the uncertainty in the value of each observable captured by the Born's rule probabilities. Entropy is a quantification of randomness, and we propose a spin-entropy for the observables of spin pure states based on the phase space of a spin as described by the geometric quantization method, and we also expand it to mixed quantum states. This proposed entropy overcomes the limitations of previously-proposed entropies such as von Neumann entropy which only quantifies the randomness of specifying the quantum state. As an example of a limitation, previously-proposed entropies are higher for Bell entangled spin states than for disentangled spin states, even though the spin observables are less constrained for a disentangled pair of spins than for an entangled pair. The proposed spin-entropy accurately quantifies the randomness of a quantum state, it never reaches zero value, and it is lower for entangled states than for disentangled states.
cnot Gates for Fluxonium Qubits via Selective Darkening of Transitions

We analyze the cross-resonance effect for fluxonium circuits and investigate a two-qubit gate scheme based on selective darkening of a transition. In this approach, two microwave pulses at the frequency of the target qubit are applied simultaneously with a proper ratio between their amplitudes to achieve a controlled-NOT operation. We study in detail coherent gate dynamics and calculate gate error. With nonunitary effects accounted for, we demonstrate that gate error below $10^{-4}$ is possible for realistic hardware parameters. This number is facilitated by long coherence times of computational transitions and strong anharmonicity of fluxoniums, which easily prevents excitation to higher excited states during the gate microwave drive.
Unlinkable Delegation of WebAuthn Credentials

The W3C’s WebAuthn standard employs digital signatures to offer phishing protection and unlinkability on the web using authenticators which manage keys on behalf of users. This introduces challenges when the account owner wants to delegate certain rights to a proxy user, such as to access their accounts or perform actions on their behalf, as delegation must not undermine the decentralisation, unlinkability, and attestation properties provided by WebAuthn. We present two approaches, called remote and direct delegation of WebAuthn credentials, maintaining the standard’s properties. Both approaches are compatible with Yubico’s recent Asynchronous Remote Key Generation (ARKG) primitive proposed for backing up credentials. For remote delegation, the account owner stores delegation credentials at the relying party on behalf of proxies, whereas the direct variant uses a delegation-by-warrant approach, through which the proxy receives delegation credentials from the account owner and presents them later to the relying party. To realise direct delegation we introduce Proxy Signature with Unlinkable Warrants (PSUW), a new proxy signature scheme that extends WebAuthn’s unlinkability property to proxy users and can be constructed generically from ARKG. We provide an instantiation of the primitive and analyse its performance, observing only a minor increase of a few milliseconds in the signing and verification times for delegated WebAuthn credentials based on the ARKG and PSUW primitives.
Proactive Stateful Fault-Tolerant System for Kubernetes Containerized Services

Recently, the development of Kubernetes (K8s) containerization platform has enabled cloudbased, lightweight, highly scalable, and agile services in both general and telco use-cases.Ensuring high availability, reliable and continuous containerized services is a major requirement of service providers to provide fault-tolerance, transparent service experiences to end-users.To satisfy this requirement, fault prediction and proactive stateful service recovery features must be applied in cloud systems.Prior proactive failure recovery approaches mostly focused on either improving fault prediction performance based on different machine learning time series forecasting techniques or optimizing recovery service placement after fault prediction.However, a mechanism that enables stateful containerized service migration from the predicted faulty node to the healthy destination node has not been studied.Service migration in previous proactive works is only simulated or performed by virtual machine (VM) migration techniques.In this paper, we propose a proactive stateful fault-tolerant system for K8s containerized services that pipelines a Bidirectional Long Short-Term Memory (Bi-LSTM) fault prediction framework and a novel K8s stateful service migration mechanism for service recovery.Experimental results show how the Bi-LSTM model improved prediction performance against other time-series forecasting models in prior proactive works.We then combined the Bi-LSTM fault prediction framework with both the default K8s and our stateful migration mechanisms.The comparison between these two proactive systems proves our system efficiency in terms of avoiding Quality of Service (QoS) violation.
Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records

Abstract Background Improving the identification of people who inject drugs (PWID) in electronic medical records can improve clinical decision making, risk assessment and mitigation, and health service research. Identification of PWID currently consists of heterogeneous, nonspecific International Classification of Diseases (ICD) codes as proxies. Natural language processing (NLP) and machine learning (ML) methods may have better diagnostic metrics than nonspecific ICD codes for identifying PWID. Methods We manually reviewed 1000 records of patients diagnosed with Staphylococcus aureus bacteremia admitted to Veterans Health Administration hospitals from 2003 through 2014. The manual review was the reference standard. We developed and trained NLP/ML algorithms with and without regular expression filters for negation (NegEx) and compared these with 11 proxy combinations of ICD codes to identify PWID. Data were split 70% for training and 30% for testing. We calculated diagnostic metrics and estimated 95% confidence intervals (CIs) by bootstrapping the hold-out test set. Best models were determined by best F-score, a summary of sensitivity and positive predictive value. Results Random forest with and without NegEx were the best-performing NLP/ML algorithms in the training set. Random forest with NegEx outperformed all ICD-based algorithms. F-score for the best NLP/ML algorithm was 0.905 (95% CI, .786–.967) and 0.592 (95% CI, .550–.632) for the best ICD-based algorithm. The NLP/ML algorithm had a sensitivity of 92.6% and specificity of 95.4%. Conclusions NLP/ML outperformed ICD-based coding algorithms at identifying PWID in electronic health records. NLP/ML models should be considered in identifying cohorts of PWID to improve clinical decision making, health services research, and administrative surveillance.
Adversarial attacks on graph-level embedding methods: a case study

Abstract As the number of graph-level embedding techniques increases at an unprecedented speed, questions arise about their behavior and performance when training data undergo perturbations. This is the case when an external entity maliciously alters training data to invalidate the embedding. This paper explores the effects of such attacks on some graph datasets by applying different graph-level embedding techniques. The main attack strategy involves manipulating training data to produce an altered model. In this context, our goal is to go in-depth about methods, resources, experimental settings, and performance results to observe and study all the aspects that derive from the attack stage.
Colorizing the Past: Deep Learning for the Automatic Colorization of Historical Aerial Images

The colorization of grayscale images can, nowadays, take advantage of recent progress and the automation of deep-learning techniques. From the media industry to medical or geospatial applications, image colorization is an attractive and investigated image processing practice, and it is also helpful for revitalizing historical photographs. After exploring some of the existing fully automatic learning methods, the article presents a new neural network architecture, Hyper-U-NET, which combines a U-NET-like architecture and HyperConnections to handle the colorization of historical black and white aerial images. The training dataset (about 10,000 colored aerial image patches) and the realized neural network are available on our GitHub page to boost further research investigations in this field.
PatchView: A Python Package for Patch-clamp Data Analysis and Visualization

PatchView integrates multiple open-source tools and provides an intuitive graphic user interface (GUI) for navigating through the data files and analyses pipeline.It is aimed to enable users to perform most analysis quickly for the data collected in a typical patch-clamp experiment without managing a Python environment or writing any Python scripts.Please refer to the full documentation and the source code for detailed information.
Artificial Intelligence and Big Data Science in Neurocritical Care

In recent years, the volume of digitalized web-based information utilizing modern computer-based technology for data storage, processing, and analysis has grown rapidly. Humans can process a limited number of variables at any given time. Thus, the deluge of clinically useful information in the intensive care unit environment remains untapped. Innovations in machine learning technology with the development of deep neural networks and efficient, cost-effective data archival systems have provided the infrastructure to apply artificial intelligence on big data for determination of clinical events and outcomes. Here, we introduce a few computer-based technologies that have been tested across these domains.
A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones. Thus, effectively filtering out these activities has become a significant problem. In this paper, a novel machine learning based segmentation scheme with a multi-probability threshold is proposed for HAR. Threshold segmentation (TS) and slope-area (SA) approaches are employed according to the characteristics of small fluctuation of static activity signals and typical peaks and troughs of periodic-like ones. In addition, a multi-label weighted probability (MLWP) model is proposed to estimate the probability of each activity. The HAR error can be significantly decreased, as the proposed model can solve the problem that the fixed window usually contains multiple kinds of activities, while the unknown activities can be accurately rejected to reduce their impacts. Compared with other existing schemes, computer simulation reveals that the proposed model maintains high performance using the UCI and PAMAP2 datasets. The average HAR accuracies are able to reach 97.71% and 95.93%, respectively.
Visual Grounding in Remote Sensing Images

Ground object retrieval from a large-scale remote sensing image is very important for lots of applications. We present a novel problem of visual grounding in remote sensing images. Visual grounding aims to locate the particular objects (in the form of the bounding box or segmentation mask) in an image by a natural language expression. The task already exists in the computer vision community. However, existing benchmark datasets and methods mainly focus on natural images rather than remote sensing images. Compared with natural images, remote sensing images contain large-scale scenes and the geographical spatial information of ground objects (e.g., longitude, latitude). The existing method cannot deal with these challenges. In this paper, we collect a new visual grounding dataset, called RSVG, and design a new method, namely GeoVG. In particular, the proposed method consists of a language encoder, image encoder, and fusion module. The language encoder is used to learn numerical geospatial relations and represent a complex expression as a geospatial relation graph. The image encoder is applied to learn large-scale remote sensing scenes with adaptive region attention. The fusion module is used to fuse the text and image feature for visual grounding. We evaluate the proposed method by comparing it to the state-of-the-art methods on RSVG. Experiments show that our method outperforms the previous methods on the proposed datasets. https://sunyuxi.github.io/publication/GeoVG
Effective Multimodal Encoding for Image Paragraph Captioning

In this paper, we present a regularization-based image paragraph generation method. We propose a novel multimodal encoding generator (MEG) to generate effective multimodal encoding that captures not only an individual sentence but also visual and paragraph-sequential information. By utilizing the encoding generated by MEG, we regularize a paragraph generation model that allows us to improve the results of the captioning model in all the evaluation metrics. With the support of the proposed MEG model for regularization, our paragraph generation model obtains state-of-the-art results on the Stanford paragraph dataset once further optimized with reinforcement learning. Moreover, we perform extensive empirical analysis on the capabilities of MEG encoding. A qualitative visualization based on t-distributed stochastic neighbor embedding (t-SNE) illustrates that sentence encoding generated by MEG captures some level of semantic information. We also demonstrate that the MEG encoding captures meaningful textual and visual information by performing multimodal sentence retrieval tasks and image instance retrieval given a paragraph query.
Development and Validation of Machine Learning Models for Prediction of Fracture Risk in Patients with Elderly-Onset Rheumatoid Arthritis

Objective: Fracture is a critical unfavorable prognostic factor in patients with rheumatoid arthritis(RA) and osteoporosis. At present, models involving clinical indices that accurately predict fracture are still uncommon. We addressed this gap by developing machine learning (ML)-based predictive models to individualize the risk of fracture in elderly patients with RA and osteoporosis and to identify a high-risk group for fracture. Methods: 487 patients diagnosed with RA and osteoporosis at the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture were randomly divided into a training cohort (used for building the model) and a validation cohort (used for validating the model). Five ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model. Results: A total of twenty-two candidate variables were included, and the prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, artificial neural network (ANN), support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), probability of major osteoporotic fractures (PMOF), and probability of hip fracture (PHF) ranged from 0.695 to 0.878. Among them, RFC obtained the optimal prediction efficiency via adding serum selenium and clinical indices, that is, glucocorticoid, and erythrocyte sedimentation rate (ESR). Conclusion: Based on the classic clinical parameters, the fracture risk of RA patients with osteoporosis can be accurately predicted. In particular, RFC prediction model shows good discrimination ability in identifying high-risk patients with fracture. Keywords: rheumatoid arthritis, osteoporosis, fracture, machine learning algorithm, risk factor, predictive model
Cyclic controlled remote state preparation in the three-dimensional system

Abstract In this work, we propose a novel protocol for cyclic controlled remote state preparation (CCRSP) with single-qutrit equatorial states. In this protocol, three distant parties, Alleyn, Breden and Carden, are not only senders but also receivers. Alleyn wants to remotely prepare a single-qutrit equatorial state at Breden’s site, while at the same time, Breden (Carden) wishes to help Carden (Alleyn) remotely preparing another single-qutrit equatorial state. It is shown that, only if the three senders and the controller collaborate with each other, the CCRSP can be completed. To present this protocol more clearly, only maximally entangled channel is considered.
BRIGHT - Graph Neural Networks in Real-time Fraud Detection

Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph. However, two challenges arise in the implementation of GNNs in production. First, future information in a dynamic graph should not be considered in message passing to predict the past. Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. To tackle these challenges, we propose a Batch and Real-time Inception GrapH Topology (BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient online real-time inference.
An improved estimator of Shannon entropy with applications to systems with memory

We investigate the memory properties of discrete sequences built upon a finite number of states. We find that the block entropy can reliably determine the memory for systems modeled as Markov chains of arbitrary finite order. Further, we provide an entropy estimator that remarkably gives accurate results when correlations are present. To illustrate our findings, we calculate the memory of daily precipitation series at different locations. Our results are in agreement with existing methods being at the same time valid in the undersampled regime and independent of model selection.
ALTOCUMULUS: Scalable Scheduling for Nanosecond-Scale Remote Procedure Calls

Online services in modern datacenters use Remote Procedure Calls (RPCs) to communicate between different software layers. Despite RPCs using just a few small functions, inefficient RPC handling can cause delays to propagate across the system and degrade end-to-end performance. Prior work has reduced RPC processing time to less than 1 $\mu$ s, which now shifts the bottleneck to the scheduling of RPCs. Existing RPC schedulers suffer from either high overheads, inability to effectively utilize high core-count CPUs or do not adaptively fit different traffic patterns. To address these shortcomings, we present ALTOCUMULUS, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> a scalable, software-hardware codesign to schedule RPCs at nanosecond scales. ALTOCUMULUS provides a proactive scheduling scheme and low-overhead messaging mechanism on top of a decentralized user runtime. ALTOCUMULUS also offers direct access from the user space to a set of simple hardware primitives to quickly migrate long-latency RPCs. We evaluate ALTOCUMULUS with synthetic workloads and an end-to-end in-memory key-value store application under real-world traffic patterns. ALTOCUMULUS improves throughput by 1.3-24.6$\times$ under a 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile latency <300$\mu$ s and reduces tail latency by up to 15.8$\times$ on 16-core systems over current state-of-the-art software and hardware schedulers. For 256-core systems, integrating ALTOCUMULUS with either a hardware-optimized NIC or commodity PCIe NIC can improve throughput by $ 2.8\times$ or $ 2.7\times$, respectively, under 99 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile latency $\lt 8.5\mu \mathrm{s}$. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Automatic Concurrent Migration Load-balancing Strategy (AutoCuMuLuS), homophonic with “altocumulus” as a type of clouds in meteorology, fragmented to separate patches or nodes.
A Role for HTN Planning in Increasing Trust in Autonomous Driving

The adoption of autonomous vehicles mainly depends on the driver's trust in the vehicle's capabilities. Influencing trust requires giving it a central role when designing the vehicle's functionalities, including the one for driving from one location to another. Addressing this driving task requires not only considering environmental and vehicle's conditions (e.g., road obstacles, fuel level, but also factors that influence trust, such as variability of trust, use of understandable and structured knowledge, and operation transparency. One way to address such a driving task is to solve it as a planning problem. Among AI planning techniques, Hierarchical Task Network (HTN) planning provides a powerful approach to model rich domain knowledge using hierarchical constructs, simulating the way in which one conceptualises knowledge and performs decision making. Here, we analyse the suitability of HTN planning for the trust-based driving task and define the respective planning problem. Based on this, we model an HTN domain for the driving task and use it to solve the driving task in two case studies. The results indicate that trust-based HTN planning provides a feasible approach for efficiently computing plans that maximise trust.