Reviewerly/exHarmony huggingface dataset:
FL-MISR: fast large-scale multi-image super-resolution for computed tomography based on multi-GPU acceleration

Abstract Multi-image super-resolution (MISR) usually outperforms single-image super-resolution (SISR) under a proper inter-image alignment by explicitly exploiting the inter-image correlation. However, the large computational demand encumbers the deployment of MISR in practice. In this work, we propose a distributed optimization framework based on data parallelism for fast large-scale MISR using multi-GPU acceleration named FL-MISR. The scaled conjugate gradient (SCG) algorithm is applied to the distributed subfunctions and the local SCG variables are communicated to synchronize the convergence rate over multi-GPU systems towards a consistent convergence. Furthermore, an inner-outer border exchange scheme is performed to obviate the border effect between neighboring GPUs. The proposed FL-MISR is applied to the computed tomography (CT) system by super-resolving the projections acquired by subpixel detector shift. The SR reconstruction is performed on the fly during the CT acquisition such that no additional computation time is introduced. FL-MISR is extensively evaluated from different aspects and experimental results demonstrate that FL-MISR effectively improves the spatial resolution of CT systems in modulation transfer function (MTF) and visual perception. Comparing to a multi-core CPU implementation, FL-MISR achieves a more than 50 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>×</mml:mo></mml:math> speedup on an off-the-shelf 4-GPU system.
Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks

Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them without extensive feature engineering. Prior work has shown how to generate network embeddings while preserving the structural role proximity of nodes. These methods, however, cannot capture the temporal evolution of the structural identity of the nodes in dynamic networks. Other works, on the other hand, have focused on learning microscopic dynamic embeddings. Though these methods can learn node representations over dynamic networks, these representations capture the local context of nodes and do not learn the structural roles of nodes. In this article, we propose a novel method for learning structural node embeddings in discrete-time dynamic networks. Our method, called HR2vec , tracks historical topology information in dynamic networks to learn dynamic structural role embeddings. Through experiments on synthetic and real-world temporal datasets, we show that our method outperforms other well-known methods in tasks where structural equivalence and historical information both play important roles. HR2vec can be used to model dynamic user behavior in any networked setting where users can be represented as nodes. Additionally, we propose a novel method (called network fingerprinting) that uses HR2vec embeddings for modeling whole (or partial) time-evolving networks. We showcase our network fingerprinting method on synthetic and real-world networks. Specifically, we demonstrate how our method can be used for detecting foreign-backed information operations on Twitter.
Efficient implementation of Montgomery modular multiplier on FPGA

Recent developments in embedded devices have enhanced the demand for systems using compact cryptographic modules. Modular multiplication with large modulus is a central procedure in many public-key cryptosystems. This work describes a creative structure of an FPGA hardware architecture to compute the modular multiplication of two integers. The proposed design has enhanced the hardware structure of Montgomery Modular Multiplier (MMM) in a more efficient way to increase the performance and decrease the area cost. The proposed design is coded in VHDL, and implemented in real-time on the Nexys-3 board. The synthesis results of implementing the proposed 256-bit and 1024-bit modular multiplier on Virtex-6 FPGA have a computation time 1.79 µs and 20.53 µs, occupies 1104 LUTs and 4450 LUTs, and runs at 143.82 MHz and 49 MHz, respectively. Compared to other related FPGA implementations, the proposed design uses less FPGA resources with better efficiency and lower Area Time per bit-length (AT/b).
Transferability Estimation using Bhattacharyya Class Separability

Transfer learning has become a popular method for leveraging pre-trained models in computer vision. However, without performing computationally expensive fine-tuning, it is difficult to quantify which pre-trained source models are suitable for a specific target task, or, conversely, to which tasks a pre-trained source model can be easily adapted to. In this work, we propose Gaussian Bhattacharyya Coefficient (GBC), a novel method for quantifying transferability between a source model and a target dataset. In a first step we embed all target images in the feature space defined by the source model, and represent them with per-class Gaussians. Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task. We evaluate GBC on image classification tasks in the context of dataset and architecture selection. Further, we also perform experiments on the more complex semantic segmentation transferability estimation task. We demonstrate that GBC outperforms state-of-the-art transferability metrics on most evaluation criteria in the semantic segmentation settings, matches the performance of top methods for dataset transferability in image classification, and performs best on architecture selection problems for image classification.
Contextuality and Wigner Negativity Are Equivalent for Continuous-Variable Quantum Measurements

Quantum computers will provide considerable speedups with respect to their classical counterparts. However, the identification of the innately quantum features that enable these speedups is challenging. In the continuous-variable setting - a promising paradigm for the realisation of universal, scalable, and fault-tolerant quantum computing - contextuality and Wigner negativity have been perceived as two such distinct resources. Here we show that they are in fact equivalent for the standard models of continuous-variable quantum computing. While our results provide a unifying picture of continuous-variable resources for quantum speedup, they also pave the way towards practical demonstrations of continuous-variable contextuality, and shed light on the significance of negative probabilities in phase-space descriptions of quantum mechanics.
Round-Efficient Byzantine Agreement and Multi-party Computation with Asynchronous Fallback

Protocols for Byzantine agreement (BA) and secure multi-party computation (MPC) can be classified according to the underlying communication model. The two most commonly considered models are the synchronous one and the asynchronous one. Synchronous protocols typically lose their security guarantees as soon as the network violates the synchrony assumptions. Asynchronous protocols remain secure regardless of the network conditions, but achieve weaker security guarantees even when the network is synchronous.Recent works by Blum, Katz and Loss [TCC’19], and Blum, Liu-Zhang and Loss [CRYPTO’20] introduced BA and MPC protocols achieving security guarantees in both settings: security up to \(t_s\) corruptions in a synchronous network, and up to \(t_a\) corruptions in an asynchronous network, under the provably optimal threshold trade-offs \(t_a \le t_s\) and \(t_a + 2t_s < n\). However, current solutions incur a high synchronous round complexity when compared to state-of-the-art purely synchronous protocols. When the network is synchronous, the round complexity of BA protocols is linear in the number of parties, and the round complexity of MPC protocols also depends linearly on the depth of the circuit to evaluate.In this work, we provide round-efficient constructions for both primitives with optimal resilience: fixed-round and expected constant-round BA protocols, and an MPC protocol whose round complexity is independent of the circuit depth.
Heterogeneous graph convolutional network with local influence

Graph convolutional networks (GCNs) have recently drawn extensive attention due to their superior learning performance on graph data. Through graph convolution, topological structure and node attributes can be simultaneously aggregated in a local neighborhood. In heterogeneous information networks (HINs), the diversity of node and edge types poses great challenges to graph convolution. This paper proposes a heterogeneous graph convolutional network based on local influence (named HIGCN), which aims to discriminatively aggregate structural information, attribute information and multi-semantic information in HINs. Here, local influence refers to the influence of neighborhood nodes on the central node. Firstly, a HIGCN block is constructed, in which the local influence is calculated through a heuristic structural influence strategy proposed in this paper and an attention-based attribute influence strategy. Afterwards, 1 × 1 convolution is innovatively used to fuse the embeddings under multiple semantics. Finally, the entire HIGCN framework is constructed by stacking HIGCN blocks. Experiments on real-world network datasets show that HIGCN achieves higher accuracy than related methods in various downstream tasks (node classification, link prediction, etc.), which verifies the effectiveness of the structural influence strategy and the semantic fusion method.
Predicting rare events of solar power production with the analog ensemble

Some revised versions of the analog ensemble (AnEn) algorithm to improve its performance when predicting rare events of solar power production are presented. Rare events are considered both extremely cloudy situations and clear sky conditions with very low aerosol concentrations. In these cases, the solar power production can be respectively unusually low or high, making it more difficult to find good analog dates in a historical archive of past forecasts. The lack of suitable quality analogs determines conditional biases from the AnEn when predicting these rare events. Here, an application of the AnEn to predict solar power generated by two photovoltaic plants located in Kuwait is presented. The modified versions of the AnEn are compared to the standard algorithm. We focus the verification of the AnEn's performance over the rare conditions outlining the improvements achieved with the new AnEn models.
Efficient Uncertainty Estimation in Semantic Segmentation via Distillation

Deep neural networks typically make predictions with little regard for the probability that a prediction might be incorrect. Attempts to address this often involve input data undergoing multiple forward passes, either of multiple models or of multiple configurations of a single model, and consensus among outputs is used as a measure of confidence. This can be computationally expensive, as the time taken to process a single input sample increases linearly with the number of output samples being generated, an important consideration in real-time scenarios such as autonomous driving, and so we propose Uncertainty Distillation as a more efficient method for quantifying prediction uncertainty. Inspired by the concept of Knowledge Distillation, whereby the performance of a compact model is improved by training it to mimic the outputs of a larger model, we train a compact model to mimic the output distribution of a large ensemble of models, such that for each output there is a prediction and a predicted level of uncertainty for that prediction. We apply Uncertainty Distillation in the context of a semantic segmentation task for autonomous vehicle scene understanding and demonstrate a capability to reliably predict pixelwise uncertainty over the resultant class probability map. We also show that the aggregate pixel uncertainty across an image can be used as a metric for reliable detection of out-of-distribution data.
Exploring digital economy: a sociosemiotic perspective

Abstract Digital technologies have transformed our lives with unimaginable speed and scale, delivering immense opportunities and daunting challenges and leading to the birth of the digital economy. China and the United States (US) are two leading countries in the digital economy in both size and growth rate. This study employs both quantitative and qualitative methods to investigate the similarities and differences between the US and Chinese legislation from a sociosemiotic perspective. By comparing the high frequency words in the two purpose-built corpora, it can be noted that as digital economy, a social sign, has the characteristics of spatiality and temporality. The US federal legislation related to digital economy focuses more on security and protection and has more specific regulations in individual industries, while Chinese legislation is more concerned with the strategy and guideline of development of industries and technologies in digital economy. In the meantime, information infrastructure and information and communication technologies are identified as the foundation and core elements shared in the two countries’ digital economies. Such a corpus-based sociosemiotic exploration of digital economy can shed light on relevant studies in the discourse analysis of legal texts.
The structural relationships among spatial presence, situational interest and behavioral attitudes toward online virtual museum navigation: a PLS-SEM analysis

Purpose This study proposed a research model for verifying the structural relationships among users' perceived spatial presence (VR affordance), situational interest (affective factor) and behavioral attitudes (outcome) toward online virtual museum navigation. What role of situational interest plays in the relationships between spatial presence and outcomes was further examined. Design/methodology/approach There were 141 adults invited to participate in the research trials on the navigation of the online virtual museum which was established by 3D space capture techniques. This study conducted a series of PLS-SEM to analyze the participants' quantitative responses to the surveys of spatial presence, situational interest and attitudes. Findings The testing of reliability and validity for the measurement model of this study was satisfied. The significance of users' perceived situational interest: attention demand, instant enjoyment and exploration intention when navigating the online virtual museum with perceptions of spatial presence were identified. The mediation of situational interest in the relationships between spatial presence and behavioral attitudes were also verified. Practical implications For the practical design of desktop-based online virtual museums, it is suggested that more engaging strategies such as narrative storytelling and gamified interaction could be integrated to enhance users' situational interest including attention demand, instant enjoyment and exploration intention. Originality/value Through the examination of situational interest from multiple constructs, the research model of this study advanced the conceptual framework for understanding the consequences of VR affordance in online virtual museum navigation. The theoretical contribution to verifying the mediated role of situational interest was made.
The underwater polarization dehazing imaging with a lightweight convolutional neural network

The scattering and absorption of particles in underwater environment seriously affect the quality of underwater images, resulting in reduced contrast and imaging quality. In this work, the underwater active polarization dehazing imaging based on the deep learning model is studied. A modified lightweight dehazing convolutional neural network (CNN) model with four input channels is designed by combining both the advantages of the deep learning and polarization dehazing imaging technology. The lightweight CNN is trained and tested with the images of different polarization components (00, 450, 900 linear polarization and circular polarization) in different turbidity underwater environments. The experimental results show that this method can rapidly achieve the better dehazing imaging effect than that of conventional dehazing methods.
A verifiable multi-party quantum key distribution protocol based on repetitive codes

A multi-party quantum key distribution protocol based on repetitive code is designed in this paper for the first time. First we encode the classical key sequence in accordance with this repetitive code. Then unitary transformation of the quantum state sequence corresponding to this encoded sequence is carried out by using the parameters from this (t, n) threshold protocol. Furthermore, we derive two thresholds for whether or not reserving the measured values of the received sequence, and extract the classical subkey sequence from the measured values conforming to these two threshold conditions. This protocol can authenticate the identity of the participant, resist the attack from the internal and external participants, and do not need the decoy state particles when testing the eavesdropper, which is more efficient than the similar protocols, and also saves the quantum resources.
MPAN: Multi-parallel attention network for session-based recommendation

A powerful session-based recommender can typically explore the users' evolving interests (i.e., a combination of her long-term and short-term interests). Recent advances in attention mechanisms have led to state-of-the-art methods for solving this task. However, there are still three main limitations. First, most of the attention-based methods only utilize the last clicked item to represent users' short-term interests which ignores the temporal information and behavior context. Second, to learn users' long-term interests, most existing models employ the vanilla attention method, which has difficulty in capturing the diversity of long-term interests. Third, current studies typically assume that long-term interests and short-term interests are equally important ignoring their user-specific importance. Therefore, we propose a Multi-Parallel Attention Network (MPAN) model for Session-based Recommendation. Specifically, we propose a novel time-aware attention mechanism to learn users' short-term interests by capturing contextual information and temporal signals simultaneously. Next, we design a refined multi-head attention mechanism to extract the diverse long-term interests from different latent subspaces. Besides, we introduce a gated fusion method that adaptively integrates users' long-term and short-term interests to generate a hybrid interest representation. Experiments on three real-world datasets show that MPAN achieves noticeable improvements than state-of-the-art methods.
Feasibility of Bluetooth Low Energy wearable tags to quantify healthcare worker proximity networks and patient close contact: A pilot study

The hospital environment is characterised by a dense network of interactions between healthcare workers (HCWs) and patients. As highlighted by the coronavirus pandemic, this represents a risk for disease transmission and a challenge for contact tracing. We aimed to develop and pilot an automated system to address this challenge and describe contacts between HCWs and patients.We developed a bespoke Bluetooth Low Energy (BLE) system for the hospital environment with anonymous tags worn by HCWs and fixed receivers at patient room doors. Proximity between wearable tags inferred contact between HCWs. Tag-receiver interactions inferred patient room entry and exit by HCWs. We performed a pilot study in four negative pressure isolation rooms from 13 April to 18 April 2021. Nursing and medical staff who consented to participate were able to collect one of ten wearable BLE tags during their shift.Over the four days, when divided by shift times, 27 nursing tags and 3 medical tags were monitored. We recorded 332 nurse-nurse interactions, for a median duration of 58 s [interquartile range (IQR): 39-101]. We recorded 45 nursing patient room entries, for a median 7 min [IQR: 3-21] of patient close contact. Patient close contact was shorter in rooms on airborne precautions, compared to those not o transmission-based precautions.This pilot study supported the functionality of this approach to quantify HCW proximity networks and patient close contact. With further refinements, the system could be scaled-up to support contact tracing in high-risk environments.
A Predictive Energy Consumption Scheduling Algorithm for Multiprocessor Heterogeneous System

With the increasing number of users and application in the era of the Industrial Internet of Things (I-IoT), computing efficiency and energy consumption become two vital problems. Multi-processor heterogeneous system has been widely used for handling parallel workflow applications to advance the performance of system, in which efficient workflow scheduling and the choice of frequency for each task play key roles. However, most scheduling algorithms just focus on an optimal solution for every single task and may exclude optimal task priority sequence at the task sorting phase. Therefore, this paper proposes a Predictive Energy Consumption Scheduling (PECS) algorithm to match the frequency for each task. Firstly, the alterable time space is introduced to decompose the deadline of the whole workflow application into each task. Secondly, as the basis of allocation, Predictive Energy Consumption Matrix (ECM) is defined to predict the impact of current task allocation on the energy consumption of subsequent tasks. Thirdly, the processor with the lowest predicted energy consumption is matched for each task to form a preliminary scheduling scheme with the constraint of time. Finally, all tasks in the same priority level of a processor are swapped in order, then a scheduling strategy is formed to minimize energy consumption. Experimental results reveal that our proposed algorithm can reduce energy 48.28% and 13.33% averagely compared with other two state of the art algorithms.
Providing the Competitiveness of Education due to the Formation of Professional Competence via the Project-Based Learning Technology

The paper highlights the innovative approach to providing the competitiveness of education. The idea of the productivity of project-based learning on the basis of integrative disciplines as a holistic reflection of the unity of professional and humanities knowledge in the scientific picture is presented. Academic disciplines are the integration of basic sciences at the applied level. Mathematical formalization, due to the high degree of generality of its apparatus, is recognized as the leading means of forming integrative content of educational competitiveness. The need for a new type of rationality is substantiated, which will provide the coordination of logic with the purpose of human existence and is able to combine rational and irrational forms of comprehension of reality and expand the scope of mathematical formalization. It is claimed that the decisive role in making a project decision belongs to the subject. A comparison of traditional and project-based learning according to the main criteria and practical advice on the areas of project implementation are presented.