Reviewerly/exHarmony huggingface dataset:
Sampling With Replacement vs Poisson Sampling: A Comparative Study in Optimal Subsampling

Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to improve estimation efficiency. For computational efficiency, subsampling is often implemented with replacement or through Poisson subsampling. However, no rigorous investigation has been performed to study the difference between the two subsampling procedures such as their estimation efficiency and computational convenience. This paper performs a comparative study on these two different sampling procedures. In the context of maximizing a general target function, we first derive asymptotic distributions for estimators obtained from the two sampling procedures. The results show that the Poisson subsampling may have a higher estimation efficiency. Based on the asymptotic distributions for both subsampling with replacement and Poisson subsampling, we derive optimal subsampling probabilities that minimize the variance functions of the subsampling estimators. These subsampling probabilities further reveal the similarities and differences between subsampling with replacement and Poisson subsampling. The theoretical characterizations and comparisons on the two subsampling procedures provide guidance to select a more appropriate subsampling approach in practice. Furthermore, practically implementable algorithms are proposed based on the optimal structural results, which are evaluated through both theoretical and empirical analyses.
unXpec: Breaking Undo-based Safe Speculation

Speculative execution attacks exploiting speculative execution to leak secrets have aroused significant concerns in both industry and academia. They mainly exploit covert or side channels over microarchitectural states left by mis-speculated and squashed instructions (i.e., transient instructions). Most such attacks target cache states. Existing cache-based defenses against speculative execution attacks fall into two categories, Invisible and Undo. Most Invisible defenses buffer execution metadata of speculative instructions and place them into the cache only if the speculatively executed instructions become determined. Motivated by the fact that mis-speculations are rare cases, Undo defenses allow speculative instructions to modify cache states. Upon a mis-speculation, they rollback cache states to the ones prior to the execution of transient instructions. However, Invisible defenses have been recently found insecure by the speculative interference attack. This calls for a deep security inspection of Undo defenses against speculative execution attacks.In this paper, we present unXpec as the first attack against Undo-based safe speculation. It exploits the secret-dependent timing channel exhibited through the rollback operations of Undo defenses. Specifically, the rollback process requires both invalidating cache lines brought into the cache by transient instructions and restoring evicted cache lines from the cache by transiently loaded data. This opens up a channel that encodes secret via the timing difference between when rollback involves much invalidation and restoration or not. We further leverage eviction sets to enforce more restoration operations. This yields a longer rollback time and thus a larger secret-dependent timing difference. We demonstrate the timing channel over the open-source CleanupSpec, a representative Undo solution. A single transient load can trigger a secret-dependent timing difference of 22 cycles (without eviction sets) of 32 cycles (with eviction sets), which is sufficiently exploitable for constructing a covert channel for speculative execution attacks. We run unXpec on the gem5 simulator with CleanupSpec enabled. The results show that unXpec can leak secrets at a high rate of 140 Kbps with an accuracy over 90%. Simply enforcing constant-time rollback to mitigate unXpec may induce an over 70% performance overhead.
Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost

Early prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.CCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days-1 year, 1-5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. Subsequently, Local Interpretable Model-Agnostic Explanations method was performed to improve XGBoost model interpretability. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analyzed using a clinical impact curve. At last, feature ablation curves of the XGBoost model were conducted to obtain the simplified model.Overall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features.For CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.
Weak Randomness Analysis of Measurement-Device-Independent Quantum Key Distribution with Finite Resources

The ideal quantum key distribution (QKD) protocol requires perfect random numbers for bit encoding and basis selecting. Perfect randomness is of great significance to the practical QKD system. However, due to the imperfection of practical quantum devices, an eavesdropper (Eve) may acquire some random numbers, thus affecting the security of practical systems. In this paper, we analyze the effects of the weak randomness in the measurement-device-independent QKD (MDI-QKD) with finite resources. We analytically derive concise formulas for estimating the lower bound of the single-photon yield and the upper bound of the phase error rate in the case of the weak randomness. The simulation demonstrates that the final secret key rate of MDI-QKD with finite resources is sensitive to state preparation, even with a small proportion of weak randomness, the secure key rate has a noticeable fluctuation. Therefore, the weak randomness of the state preparation may bring additional security risks. In order to ensure the practical security of the QKD system, we are supposed to strengthen the protection of state preparation devices.
Evaluating 3D Human Motion Capture on Mobile Devices

Computer-vision-based frameworks enable markerless human motion capture on consumer-grade devices in real-time. They open up new possibilities for application, such as in the health and medical sector. So far, research on mobile solutions has been focused on 2-dimensional motion capture frameworks. 2D motion analysis is limited by the viewing angle of the positioned camera. New frameworks enable 3-dimensional human motion capture and can be supported through additional smartphone sensors such as LiDAR. 3D motion capture promises to overcome the limitations of 2D frameworks by considering all three movement planes independent of the camera angle. In this study, we performed a laboratory experiment with ten subjects, comparing the joint angles in eight different body-weight exercises tracked by Apple ARKit, a mobile 3D motion capture framework, against a gold-standard system for motion capture: the Vicon system. The 3D motion capture framework exposed a weighted Mean Absolute Error of 18.80° ± 12.12° (ranging from 3.75° ± 0.99° to 47.06° ± 5.11° per tracked joint angle and exercise) and a Mean Spearman Rank Correlation Coefficient of 0.76 for the whole data set. The data set shows a high variance of those two metrics between the observed angles and performed exercises. The observed accuracy is influenced by the visibility of the joints and the observed motion. While the 3D motion capture framework is a promising technology that could enable several use cases in the entertainment, health, and medical area, its limitations should be considered for each potential application area.
Image metadata. From information management to interpretative practice

This study focuses the theoretical underpinning of the so-called semantic gap. By analysing the discourses on ideals and practices of image search and image use in terms of different understandings of ‘information’ and ‘communication’ this study illuminates the epistemological foundation of different ways of thinking about image descriptions. More precisely, it compares the discourse for metadata production, standards and information management with the discourse among humanities scholars. Close readings of handbooks, best practice and interviews with metadata producers discloses a discourse imbued by a mechanical model for communication and information transmission and a focus on objectivity and effectiveness. Simultaneously, interviews with humanities scholars and close readings of recent archival theory reveal another understanding of metadata as historically, institutionally, and even individually situated interpretations. As this study shows, attentiveness to the theoretical underpinning of the different ideals, wants and needs of metadata for images may illuminate why these differences exists.
Adjustable super-resolution network via deep supervised learning and progressive self-distillation

With the use of convolutional neural networks, Single-Image Super-Resolution (SISR) has advanced dramatically in recent years. However, we notice a phenomenon that the structure of all these models must be consistent during training and testing. This severely limits the flexibility of the model, making the same model difficult to be deployed on different sizes of platforms (e.g., computers, smartphones, and embedded devices). Therefore, it is crucial to develop a model that can adapt to different needs without retraining. To achieve this, we propose a lightweight Adjustable Super-Resolution Network (ASRN). Specifically, ASRN consists of a series of Multi-scale Aggregation Blocks (MABs), which is a lightweight and efficient module specially designed for feature extraction. Meanwhile, the Deep Supervised Learning (DSL) strategy is introduced into the model to guarantee the performance of each sub-network and a novel Progressive Self-Distillation (PSD) strategy is proposed to further improve the intermediate results of the model. With the help of DSL and PSD strategies, ASRN can achieve elastic image reconstruction. Meanwhile, ASRN is the first elastic SISR model, which shows good results after directly changing the model size without retraining.
Time-Efficient Coverage Path Planning for Energy-Constrained UAV

Unmanned aerial vehicles (UAVs) have the characteristics of high mobility and wide coverage, making them widely used in coverage, search, and other fields. In these applications, UAV often has limited energy. Therefore, planning a time-efficient coverage path for energy-constrained UAV to cover the area of interest is the core issue. The existing coverage path planning algorithms assume that the UAV moves at a constant speed, without taking into account the cost of turns (including deceleration, turning, and acceleration), which is unrealistic. To solve the above problem, we propose a time-efficient coverage path planning (TECPP) algorithm for the energy-constrained UAV. We build a novel gadget-based graph model, which formalizes the time and energy costs of the flight path including straight flights and making turns (deceleration, turning, and acceleration). Moreover, our graph model is suitable for irregular-shaped areas with multiple obstacles. Finally, we transform the above problem into a generalized traveling salesman problem (GTSP) and use the generalized large neighborhood search (GLNS) solver to solve it. The experimental results show that TECPP outperforms the existing coverage path planning algorithms, and TECPP saves at least 21.6% of time.
All particles driving particle swarm optimization: Superior particles pulling plus inferior particles pushing

In particle swarm optimization (PSO), the velocity vector is a conjecture to the descending direction of the objective function. The traditional PSO obtains such a direction using only two attractors (i.e., p b and p g ). In fact, all particles may carry useful information. The particles with good fitness are capable of guiding other particles to explore promising regions; meanwhile, the particles with poor fitness values are capable of indicating the possible hopeless regions. To make use of the information carried by the whole population, an all particles driving PSO (APD-PSO) is proposed in this paper. APD-PSO utilizes superior particles as attractors and inferior particles as repellers when updating the velocity. An information interaction operator is also developed in this paper for better modeling the fitness landscape and bringing helpful noise. Comprehensive simulation experiments with statistical analysis on the results validate the excellent performance of our proposed APD-PSO. The experimental comparison to several PSO competitors shows that the proposed APD-PSO achieves very competitive optimization performance.
Topology Optimization of Operational Amplifier in Continuous Space via Graph Embedding

Operational amplifier is a key building block in analog circuits. However, the design process of the operational amplifier is complex and time-consuming, as there are no practical automation tools available in the industry. This paper presents a new topology optimization method for operational amplifiers. The behavioral description of the operational amplifier is described using a directed acyclic graph (DAG), which is then transformed into a low-dimensional embedding in continuous space using a variational graph autoencoder. Topology search is performed in the continuous embedding space using stochastic optimization methods, such as Bayesian Optimization. The yield search results are then transformed back to operational amplifier topologies using a graph decoder. The proposed method is also equipped with a surrogate model for performance prediction. Experimental results show that the proposed approach can achieve significant speedup over the genetic searching algorithms. The produced three-stage operational amplifiers offer competitive performance compared to manual designs.
MODEL THEORY AND PROOF THEORY OF THE GLOBAL REFLECTION PRINCIPLE

Abstract The current paper studies the formal properties of the Global Reflection Principle, to wit the assertion “All theorems of $\mathrm {Th}$ are true,” where $\mathrm {Th}$ is a theory in the language of arithmetic and the truth predicate satisfies the usual Tarskian inductive conditions for formulae in the language of arithmetic. We fix the gap in Kotlarski’s proof from [15], showing that the Global Reflection Principle for Peano Arithmetic is provable in the theory of compositional truth with bounded induction only ( $\mathrm {CT}_0$ ). Furthermore, we extend the above result showing that $\Sigma _1$ -uniform reflection over a theory of uniform Tarski biconditionals ( $\mathrm {UTB}^-$ ) is provable in $\mathrm {CT}_0$ , thus answering the question of Beklemishev and Pakhomov [2]. Finally, we introduce the notion of a prolongable satisfaction class and use it to study the structure of models of $\mathrm {CT}_0$ . In particular, we provide a new model-theoretical characterization of theories of finite iterations of uniform reflection and present a new proof characterizing the arithmetical consequences of $\mathrm {CT}_0$ .
Label-Noise Robust Deep Generative Model for Semi-Supervised Learning

AbstractDeep generative models have demonstrated an excellent ability to generate data by learning their distribution. Despite their unsupervised nature, these models can be implemented in semi-supervised learning scenarios by treating the class labels as additional latent variables. In this article, we propose a deep generative model for semi-supervised learning that offsets label noise, which is a ubiquitous feature in large-scale datasets owing to the high cost of annotation. We assume that noisy labels are generated from true labels and employ a noise transition matrix to describe the transition. We estimate this matrix by adjusting its entries to minimize its difference with the true transition matrix and use the estimated matrix to formulate the objective function for inference, which consists of an evidence lower bound and a classification risk. However, because directly minimizing the latter with noisy labels may result in an inaccurate classifier, we propose a statistically consistent estimator for computing the classification risk solely with noisy data. Empirical results on benchmark datasets demonstrate that the proposed model improves the classification performance over that of the baseline algorithms. We also present a case study on semiconductor manufacturing. Additionally, we empirically show that the proposed model, as a generative model, is capable of reconstructing data even with noisy labels.Keywords: Consistent estimatorDeep generative modelsImportance reweightingLabel noiseNoise transition matrixSemi-supervised learning Supplementary MaterialsSupplementary document: The pdf file contains: (i) the derivation of the objective function used for the proposed model; (ii) and the experimental results for Fashion-MNIST dataset.Python code: "Code.zip" contains the codes and datasets used in this article.AcknowledgmentsThe authors would like to thank the referees, the associate editor, and the editor for reviewing this article and providing valuable comments.Additional informationFundingThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1C1B600 4511, 2020R1A4A10187747).
Energy Aware Resource Provisioning for Multi-Criteria Scheduling in Cloud Computing

In cloud, scheduling task requests on heterogeneous resources has proved to be a crucial research problem, particularly in terms of performance and energy consumption. The main challenge for cloud providers is to find the best tradeoff between two contradictory aims: improving user satisfaction and reducing energy consumption. Initially, to provide greater user satisfaction, the cloud computing data centers need to allocate resources according to the different users’ Quality of Service (QoS) preferences in a dynamic manner. Furthermore, to manage energy consumption on the cloud system during the execution of applications, the energy scaling decision should be made through a proper approach. In this paper, a multi-criteria scheduling algorithm based on fuzzy AHP-TOPSIS hybrid methodology is proposed to meet these requirements, which first deploys FAHP to rank and then selects the best cloud solutions according to the user’s requirements using FTOPSIS. It is then followed by effective execution of the user’s requests on available resources. Besides, an energy aware cloud resource provisioning approach based on the integration of horizontal scaling and dynamic voltage frequency scaling (DVFS) techniques is proposed to meet QoS requirements of all task requests and to manage energy consumption. The evaluation of this paper is run through different job requests while being subject to various constraints. The simulation results show that the proposed approach decreases the energy consumption and response time while enhancing both the resource utilization and user satisfaction compared with the SHARP and the BULLET algorithms in all conditions.
Deep Learning for Handwriting Text Recognition: Existing Approaches and Challenges

In recent years, Handwritten Text Recognition (HTR) has attracted widespread attention due to its huge applications. HTR is the process of extracting handwritten text from an image and converting it into a digital form for machine operation. Nevertheless, due to the huge differences in personal writing and the various properties of handwritten characters in multiple languages, HTR is still a challenging open research problem, and robustness and adaptability require additional improvements. The existing approaches to solve the HTR problem are usually systems based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which utilize the Connectionist Temporal Classification (CTC) objective function. However, many approaches based on attention sequence to sequence (Seq2Seq) have been proposed for the HTR task. The Seq2Seq based approaches are more flexible, suitable for the temporal nature of the text and can use different attention mechanisms to focus on the most relevant features of the input. In this paper, we provide extensive comparison of the current Deep Learning approaches for the task of HTR. Also, we outline the current problems that limits the effectiveness of these approaches.
Narrative Comprehension Guides Eye Movements in the Absence of Motion

Viewers' attentional selection while looking at scenes is affected by both top-down and bottom-up factors. However, when watching film, viewers typically attend to the movie similarly irrespective of top-down factors-a phenomenon we call the tyranny of film. A key difference between still pictures and film is that film contains motion, which is a strong attractor of attention and highly predictive of gaze during film viewing. The goal of the present study was to test if the tyranny of film is driven by motion. To do this, we created a slideshow presentation of the opening scene of Touch of Evil. Context condition participants watched the full slideshow. No-context condition participants did not see the opening portion of the scene, which showed someone placing a time bomb into the trunk of a car. In prior research, we showed that despite producing very different understandings of the clip, this manipulation did not affect viewers' attention (i.e., the tyranny of film), as both context and no-context participants were equally likely to fixate on the car with the bomb when the scene was presented as a film. The current study found that when the scene was shown as a slideshow, the context manipulation produced differences in attentional selection (i.e., it attenuated attentional synchrony). We discuss these results in the context of the Scene Perception and Event Comprehension Theory, which specifies the relationship between event comprehension and attentional selection in the context of visual narratives.