Reviewerly/exHarmony huggingface dataset:
Boundary learning by using weighted propagation in convolution network

Deep learning has driven a great progress in natural and biological image processing. However, in material science and engineering, there are often some flaws and indistinctions in material microscopic images induced from complex sample preparation, even due to the material itself, hindering the detection of target objects. In this work, we propose WPU-net that redesigns the architecture and weighted loss of U-Net, which forces the network to integrate information from adjacent slices and pays more attention to the topology in boundary detection task. Then, the WPU-net is applied into a typical material example, i.e., the grain boundary detection of polycrystalline material. Experiments demonstrate that the proposed method achieves promising performance and outperforms state-of-the-art methods. Besides, we propose a new method for object tracking between adjacent slices, which can effectively reconstruct 3D structure of the whole material. Finally, we present a material microscopic image dataset with the goal of advancing the state-of-the-art in image processing for material science.
Neural network-based prediction of the secret-key rate of quantum key distribution

Numerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.
Application of Factorial and Binomial identities in Computing and Cybersecurity

Abstract This paper focuses on the application of factorial and binomial identities in computing, communication, and cybersecurity in connection with the symmetric and asymmetric encryptions. We can use the binomial identities and theorem in factorials as an effective security algorithm to protect the computing systems, programs, and networks. In this connection, binomial expansions which are equal to multiples of 2 with non-negative exponents, and its relations are given in this article.
EDQWS: an enhanced divide and conquer algorithm for workflow scheduling in cloud

Abstract A workflow is an effective way for modeling complex applications and serves as a means for scientists and researchers to better understand the details of applications. Cloud computing enables the running of workflow applications on many types of computational resources which become available on-demand. As one of the most important aspects of cloud computing, workflow scheduling needs to be performed efficiently to optimize resources. Due to the existence of various resource types at different prices, workflow scheduling has evolved into an even more challenging problem on cloud computing. The present paper proposes a workflow scheduling algorithm in the cloud to minimize the execution cost of the deadline-constrained workflow. The proposed method, EDQWS, extends the current authors’ previous study (DQWS) and is a two-step scheduler based on divide and conquer. In the first step, the workflow is divided into sub-workflows by defining, scheduling, and removing a critical path from the workflow, similar to DQWS. The process continues until only chain-structured sub-workflows, called linear graphs, remain. In the second step which is linear graph scheduling, a new merging algorithm is proposed that combines the resulting linear graphs so as to reduce the number of used instances and minimize the overall execution cost. In addition, the current work introduces a scoring function to select the most efficient instances for scheduling the linear graphs. Experiments show that EDQWS outperforms its competitors, both in terms of minimizing the monetary costs of executing scheduled workflows and meeting user-defined deadlines. Furthermore, in more than 50% of the examined workflow samples, EDQWS succeeds in reducing the number of resource instances compared to the previously introduced DQWS method.
Application of Factorial and Binomial identities in Cybersecurity and Communications

Abstract This paper focuses on the application of factorial and binomial identities in computing, communication, and cybersecurity in connection with the symmetric and asymmetric encryptions. We can use the binomial identities and theorem in factorials as an effective security algorithm to protect the computing systems, programs, and networks. In this connection, binomial expansions which are equal to multiples of 2 with non-negative exponents, and its relations are given in this article. MSC Classification codes: 05A10, 40A05 (65B10)
X-ray Image Enhancement Based on Nonsubsampled Shearlet Transform and Gradient Domain Guided Filtering

In this paper, we propose an image enhancement algorithm combining non-subsampled shearlet transform and gradient-domain guided filtering to address the problems of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image enhancement process. First, we decompose histogram equalization and nonsubsampled shearlet transform to the original image. We get a low-frequency sub-band and several high-frequency sub-bands. Adaptive gamma correction with weighting distribution is used for the low-frequency sub-band to highlight image contour information and improve the overall contrast of the image. The gradient-domain guided filtering is conducted for the high-frequency sub-bands to suppress image noise and highlight detail and edge information. Finally, we reconstruct all the effectively processed sub-bands by the inverse non-subsampled shearlet transform and obtain the final enhanced image. The experimental results show that the proposed algorithm has good results in X-ray image enhancement, and its objective index also has evident advantages over some classical algorithms.
What is behind the buzzword for experts and laymen: Representation of “artificial intelligence” in the IT-professionals’ and non-professionals’ minds

The aim of the presented research was to define the differences between information technology (IT) professionals (ITP) and non-professionals (NP) in the way of understanding artificial intelligence (AI). The research was designed in the tradition of categorization research. In an online study participants were asked to make typicality and familiarity judgments for 50 AI exemplars. Two types of analyses were carried out, which made it possible to identify and compare the hierarchy of AI designates (graded structure) and the dimensions of their groupings. We have found that "invisible AI" exemplars were highly rated by ITP, but "visible AI" by NP. Expert knowledge allows ITP to systematize AI exemplars based on both structural and functional elements. On the other hand, laymen indicate the functions that AI-driven products perform, rather than their structures. For ITP, they are primarily algorithmic systems, while for NP they are systems that emulate the functions of living organisms.
Unveiling atom-photon quasi-bound states in hybrid plasmonic-photonic cavity

Abstract Dissipation, often associated with plasmons, leads to decoherence and is generally considered fatal for quantum nonlinearities and entanglement. Counterintuitively, by introducing a dissipative plasmonic nanoantenna into a typical cavity quantum electrodynamics (QED) system, we unveil the wide existence of the atom-photon quasi-bound state (qBS), a kind of exotic eigenstate with anomalously small decay, in the hybrid plasmonic-photonic cavity. To derive the analytical condition of atom-photon qBS, we formulate a quantized two-mode model of the local density of states by connecting the interacting uncoupled cavity modes to the macroscopic QED. With resonant plasmon-photon coupling, we showcase the single-atom qBS that improves the efficiency of single-photon generation over one order of magnitude; and the two-atom qBS that significantly enhances spontaneous entanglement generation compared with a bare photonic cavity. Notably, such single-atom and multi-atom qBS can be simultaneously accessed in realistic plasmonic-photonic cavities, providing a versatile platform for advanced quantum technologies, such as quantum light sources, quantum computation, and quantum information.
New Intent Discovery with Pre-training and Contrastive Learning

New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes.It is a critical task for the development and service expansion of a practical dialogue system.Despite its importance, this problem remains under-explored in the literature.Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate.In this paper, we provide new solutions to two important research questions for new intent discovery: (1) how to learn semantic utterance representations and (2) how to better cluster utterances.Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning.Then, we design a new contrastive loss to exploit self-supervisory signals in unlabeled data for clustering.Extensive experiments on three intent recognition benchmarks demonstrate the high effectiveness of our proposed method, which outperforms state-of-the-art methods by a large margin in both unsupervised and semi-supervised scenarios.
Research on the Multimodal Digital Teaching Quality Data Evaluation Model Based on Fuzzy BP Neural Network

We propose in this paper a fuzzy BP neural network model and DDAE-SVR deep neural network model to analyze multimodal digital teaching, establish a multimodal digital teaching quality data evaluation model based on a fuzzy BP neural network, and optimize the initial weights and thresholds of BP neural network by using adaptive variation genetic algorithm. Since the BP neural network is highly dependent on the initial weights and points, the improved genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network, reduce the time for the BP neural network to find the importance and points that satisfy the training termination conditions, and improve the prediction accuracy and convergence speed of the neural network on the teaching quality evaluation results. The entropy value method, a data-based objectivity evaluation method, is introduced as the guidance mechanism of the BP neural network. The a priori guidance sample is obtained by the entropy method. Then, the adaptive variational genetic algorithm is used to optimize the BP neural network model to learn the a priori sample knowledge and establish the evaluation model, which reduces the subjectivity of the BP neural network learning sample. To better reflect and compare the effects of the two neural network evaluation models, BP and GA-BP, the sample data were continued to be input into the original GA and BSA to obtain the evaluation results and errors; then, the evaluation results of the two evaluation models, BP and GA-BP, were compared with the evaluation results of the two algorithms, GA and BSA. It was found that the GA-BP neural network evaluation model has higher accuracy and can be used for multimodal digital teaching quality evaluation, providing a more feasible solution.
Objective Bayesian Nets for Integrating Consistent Datasets

This paper addresses a data integration problem: given several mutually consistent datasets each of which measures a subset of the variables of interest, how can one construct a probabilistic model that fits the data and gives reasonable answers to questions which are under-determined by the data? Here we show how to obtain a Bayesian network model which represents the unique probability function that agrees with the probability distributions measured by the datasets and otherwise has maximum entropy. We provide a general algorithm, OBN-cDS, which offers substantial efficiency savings over the standard brute-force approach to determining the maximum entropy probability function. Furthermore, we develop modifications to the general algorithm which enable further efficiency savings but which are only applicable in particular situations. We show that there are circumstances in which one can obtain the model (i) directly from the data; (ii) by solving algebraic problems; and (iii) by solving relatively simple independent optimisation problems.
CXAI: Explaining Convolutional Neural Networks for Medical Imaging Diagnostic

Deep learning models have been increasingly applied to medical images for tasks such as lesion detection, segmentation, and diagnosis. However, the field suffers from the lack of concrete definitions for usable explanations in different settings. To identify specific aspects of explainability that may catalyse building trust in deep learning models, we will use some techniques to demonstrate many aspects of explaining convolutional neural networks in a medical imaging context. One important factor influencing clinician’s trust is how well a model can justify its predictions or outcomes. Clinicians need understandable explanations about why a machine-learned prediction was made so they can assess whether it is accurate and clinically useful. The provision of appropriate explanations has been generally understood to be critical for establishing trust in deep learning models. However, there lacks a clear understanding on what constitutes an explanation that is both understandable and useful across different domains such as medical image analysis, which hampers efforts towards developing explanatory tool sets specifically tailored towards these tasks. In this paper, we investigated two major directions for explaining convolutional neural networks: feature-based post hoc explanatory methods that try to explain already trained and fixed target models and preliminary analysis and choice of the model architecture with an accuracy of 98% ± 0.156% from 36 CNN architectures with different configurations.
Modern Authentication Methods: A Comprehensive Survey

This paper presents a comprehensive investigation of modern authentication schemes. We start with the importance of authentication methods and the different authentication processes. Then we present the authentication criteria used and we perform a comparison of authentication methods in terms of universality, uniqueness, collectability, performance, acceptability, and spoofing. Finally, we present multi-factor authentication challenges and security issues and present future directions.
Disentangled Ontology Embedding for Zero-shot Learning

Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrinsic complexity of inter-class relationships represented in KGs. One typical feature is that a class is often related to other classes in different semantic aspects. In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects. We also contribute a new ZSL framework named DOZSL, which contains two new ZSL solutions based on generative models and graph propagation models, respectively, for effectively utilizing the disentangled ontology embeddings. Extensive evaluations have been conducted on five benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). DOZSL often achieves better performance than the state-of-the-art, and its components have been verified by ablation studies and case studies. Our codes and datasets are available at https://github.com/zjukg/DOZSL.
A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification

Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.
Serverless Data Science - Are We There Yet? A Case Study of Model Serving

Machine learning (ML) is an important part of modern data science applications. Data scientists today have to manage the end-to-end ML life cycle that includes both model training and model serving, the latter of which is essential, as it makes their works available to end-users. Systems of model serving require high performance, low cost, and ease of management. Cloud providers are already offering model serving choices, including managed services and self-rented servers. Recently, serverless computing, whose advantages include high elasticity and a fine-grained cost model, brings another option for model serving.
MEMS ultrasonic transducers for safe, low-power and portable eye-blinking monitoring

Eye blinking is closely related to human physiology and psychology. It is an effective method of communication among people and can be used in human-machine interactions. Existing blink monitoring methods include video-oculography, electro-oculograms and infrared oculography. However, these methods suffer from uncomfortable use, safety risks, limited reliability in strong light or dark environments, and infringed informational security. In this paper, we propose an ultrasound-based portable approach for eye-blinking activity monitoring. Low-power pulse-echo ultrasound featuring biosafety is transmitted and received by microelectromechanical system (MEMS) ultrasonic transducers seamlessly integrated on glasses. The size, weight and power consumption of the transducers are 2.5 mm by 2.5 mm, 23.3 mg and 71 μW, respectively, which provides better portability than conventional methods using wearable devices. Eye-blinking activities were characterized by open and closed eye states and validated by experiments on different volunteers. Finally, real-time eye-blinking monitoring was successfully demonstrated with a response time less than 1 ms. The proposed solution paves the way for ultrasound-based wearable eye-blinking monitoring and offers miniaturization, light weight, low power consumption, high informational security and biosafety.
Generalized genomic data sharing for differentially private federated learning

The success behind Machine Learning (ML) methods has largely been attributed to the quality and quantity of the available data which can spread across multiple owners. A Federated Learning (FL) from distributed datasets often provides a reliable solution that provides valuable insight. For a genomic dataset, such data have also proven to be sensitive which requires additional safety mechanisms before any sharing or ML operations. We propose a generalized gene expression data sharing method using a differentially private mechanism. Due to the large number of genes available, the data dimension is also reduced to accommodate smaller privacy budgets as we utilize an exponential mechanism to create a private histogram from numeric expression data. The output histogram can be used in any federated machine learning setting having multiple data owners. The proposed solution was submitted to genomic data security and privacy competition, iDash 2020 where it ranked third among 55 teams. We extend the proposed solution and experimented with two different machine learning algorithms and different settings. The experimental results show that it takes around 8 s to train a model while achieving 0.89 AUC with only a privacy budget of 5. The paper outlined a method to share gene expression data for Federated Learning using a privacy-preserving mechanism. Different experimental settings and recent competition results show the efficacy of the method which can be further extended to other genomic datasets and machine learning algorithms.
Yolov3-Pruning(transfer): real-time object detection algorithm based on transfer learning

Abstract In recent years, object detection algorithms have achieved great success in the field of machine vision. To pursue the detection accuracy of the model, the scale of the network is constantly increasing, which leads to the continuous increase in computational cost and a large requirement for memory. The larger network scale allows their execution to take a longer time, facing the balance between the detection accuracy and the speed of execution. Therefore, the developed algorithm is not suitable for real-time applications. To improve the detection performance of small targets, we propose a new method, the real-time object detection algorithm based on transfer learning. Based on the baseline Yolov3 model, pruning is done to reduce the scale of the model, and then migration learning is used to ensure the detection accuracy of the model. The object detection method using transfer learning achieves a good balance between detection accuracy and inference speed and is more conducive to the real-time processing of images. Through the evaluation of the dataset voc2007 + 2012, the experimental results show that the parameters of the Yolov3-Pruning(transfer): model are reduced by 3X compared with the baseline Yolov3 model, and the detection accuracy is improved, realizes real-time processing, and improves the detection accuracy.