Reviewerly/exHarmony huggingface dataset:
Image Colorization Using the Global Scene-Context Style and Pixel-Wise Semantic Segmentation

In this paper, we present an encoder-decoder architecture that exploits global and local semantics for the automatic image colorization problem. For the global semantics, the low-level encoding features are fine-tuned by the scene-context classification to integrate the global image style. Moreover, the architecture deals with the uncertainty and relations among the scene styles based on the label smoothing and pre-trained weights from Places365. For local semantics, three branches learn the mutual benefits at the pixel-level, in which average and multi-modal distributions are respectively created from regression and soft-encoding branches, while the segmentation branch determines to which object the pixel belongs. Our experiments, which involve training with the Coco-Stuff dataset and validation on DIV2K, Places365, and ImageNet, show that our results are very encouraging.
A Derivative-Free Method for Quantum Perceptron Training in Multi-layered Neural Networks

In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. Here, we depart from the classical perceptron and the elemental operations on quantum bits, i.e. qubits, so as to formulate the problem in terms of quantum perceptrons. We then make use of measurable operators to define the states of the network in a manner consistent with a Markov process. This yields a Dirac–Von Neumann formulation consistent with quantum mechanics. Moreover, the formulation presented here has the advantage of having a computational efficiency devoid of the number of layers in the network. This, paired with the natural efficiency of quantum computing, can imply a significant improvement in efficiency, particularly for deep networks. Finally, but not least, the developments here are quite general in nature since the approach presented here can also be used for quantum-inspired neural networks implemented on conventional computers.
A Privacy-Preserving Game Model for Local Differential Privacy by Using Information-Theoretic Approach

Local differential privacy (LDP) is an effective privacy-preserving model to address the problems which do not have a trusted entity.The main idea of the LDP is to add randomness in real data to guarantee individual's private sensitive information.Here, the technology of randomized response is an effective method to realize the LDP mechanism.In fact, the randomized response is a probabilistic mapping from the real data to perturbed data, which can be modeled as an information-theoretic lossy compression mechanism.What's more, the privacy budget ε has become a de facto standard to quantify the worstcase privacy leakage.However, such a metrics can not capture the question that which one is the optimal privacy mechanism in a set of equivalent ε-privacy mechanisms.Besides, the privacy and utility are closely correlated with the privacy mechanism, and existing methods do not consider the strategic adversary's behavior.In this paper, we tackle the problem of tradeoffs privacy and utility under the rational framework within an information-theoretic approach as the metrics.To address the problem, we first formulate this trade-off as a minimax information leakage problem.Then, we propose a privacy preserving attack and defense (PPAD) game framework, that is, a two-person zero-sum (TPZS) game.Further, we develop an alternating optimization algorithm to compute the saddle point of the proposed PPAD game.As a case study, we apply our method to compare several alternative ln 2-privacy mechanisms, the experimental result demonstrates that can provide an effective method to compare equivalent ε-privacy mechanisms.Furthermore, the numeric simulation result confirms that the proposed method also be useful for the protector to assess privacy disclosure risks.
Research on English Online Education Platform Based on Genetic Algorithm and Blockchain Technology

Nowadays, with the rapid development of network technology, the online examination mode has gradually replaced the traditional paper examination mode. This paper introduces computer technology into English teaching and studies and designs an English online test system for English subjects. The system introduces the design module and introduces the genetic algorithm for analysis. By using the artificial intelligence of the genetic algorithm to analyze the examination process, the online examination system completes a series of tasks from questions to examination results, making the examination work intelligent, standardized, and highly efficient. At the same time, it also gives candidates greater fairness and flexibility, making the entire examination process more efficient and convenient.
CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature

The goal of face attribute editing is altering a facial image according to given target attributes such as hair color, mustache, gender, etc. It belongs to the image-to-image domain transfer problem with a set of attributes considered as a distinctive domain. There have been some works in multi-domain transfer problem focusing on facial attribute editing employing Generative Adversarial Network (GAN). These methods have reported some successes but they also result in unintended changes in facial regions - meaning the generator alters regions unrelated to the specified attributes. To address this unintended altering problem, we propose a novel GAN model which is designed to edit only the parts of a face pertinent to the target attributes by the concept of Complementary Attention Feature (CAFE). CAFE identifies the facial regions to be transformed by considering both target attributes as well as “complementary attributes”, which we define as those attributes absent in the input facial image. In addition, we introduce a complementary feature matching to help in training the generator for utilizing the spatial information of attributes. Effectiveness of the proposed method is demonstrated by analysis and comparison study with state-of-the-art methods.
Graph Convolutional Networks for Learning with Few Clean and Many Noisy Labels

In this work we consider the problem of learning a classifier from noisy labels when a few clean labeled examples are given. The structure of clean and noisy data is modeled by a graph per class and Graph Convolutional Networks (GCN) are used to predict class relevance of noisy examples. For each class, the GCN is treated as a binary classifier, which learns to discriminate clean from noisy examples using a weighted binary cross-entropy loss function. The GCN-inferred "clean" probability is then exploited as a relevance measure. Each noisy example is weighted by its relevance when learning a classifier for the end task. We evaluate our method on an extended version of a few-shot learning problem, where the few clean examples of novel classes are supplemented with additional noisy data. Experimental results show that our GCN-based cleaning process significantly improves the classification accuracy over not cleaning the noisy data, as well as standard few-shot classification where only few clean examples are used.
GazeGraph

In this work, we present GazeGraph, a system that leverages human gazes as the sensing modality for cognitive context sensing. GazeGraph is a generalized framework that is compatible with different eye trackers and supports various gaze-based sensing applications. It ensures high sensing performance in the presence of heterogeneity of human visual behavior, and enables quick system adaptation to unseen sensing scenarios with few-shot instances. To achieve these capabilities, we introduce the spatial-temporal gaze graphs and the deep learning-based representation learning method to extract powerful and generalized features from the eye movements for context sensing. Furthermore, we develop a few-shot gaze graph learning module that adapts the `learning to learn' concept from meta-learning to enable quick system adaptation in a data-efficient manner. Our evaluation demonstrates that GazeGraph outperforms the existing solutions in recognition accuracy by 45% on average over three datasets. Moreover, in few-shot learning scenarios, GazeGraph outperforms the transfer learning-based approach by 19% to 30%, while reducing the system adaptation time by 80%.
Light Control Using Human Body Temperature Based on Arduino Uno and PIR (Passive Infrared Receiver) Sensor

This study aims to implement the PIR sensor, Arduino UNO and 2 channel relay Module to automatically turn on the lights in the classroom at the STMIK Hang Tuah Pekanbaru campus, where the classes at the Hang Tuah Pekanbaru STMIK campus still use manual switches as controllers lights on. Therefore, a device is designed that can control the lights by using movements detected by the PIR sensor and processed using a computer.This system functions to turn on the lights automatically when someone enters the classroom and turn off the lights automatically when no one is in the class. The hardware used is Arduino Uno microcontroller, PIR motion sensor, 2 channel relay module, and 1.5 volt flashlight. The software for making programs is Arduino IDE where the programming language used is the C programming language. The test results show that the PIR sensor can detect the movement of people entering or leaving a room.
Graph Theory for Modeling and Analysis of the Human Lymphatic System

The human lymphatic system (HLS) is a complex network of lymphatic organs linked through the lymphatic vessels. We present a graph theory-based approach to model and analyze the human lymphatic network. Two different methods of building a graph are considered: the method using anatomical data directly and the method based on a system of rules derived from structural analysis of HLS. A simple anatomical data-based graph is converted to an oriented graph by quantifying the steady-state fluid balance in the lymphatic network with the use of the Poiseuille equation in vessels and the mass conservation at vessel junctions. A computational algorithm for the generation of the rule-based random graph is developed and implemented. Some fundamental characteristics of the two types of HLS graph models are analyzed using different metrics such as graph energy, clustering, robustness, etc.
Detection of k-partite entanglement and k-nonseparability of multipartite quantum states

Identifying the $k$-partite entanglement and $k$-nonseparability of general $N$-partite quantum states are fundamental issues in quantum information theory. By use of computable inequalities of nonlinear operators, we present some simple and powerful $k$-partite entanglement and $k$-nonseparability criteria that works very well and allow for a simple and inexpensive test for the whole hierarchy of $k$-partite entanglement and $k$-separability of $N$-partite systems with $k$ running from $N$ down to 2. We illustrate their strengths by considering several examples in which our criteria perform better than other known detection criteria. We are able to detect $k$-partite entanglement and $k$-nonseparabilty of multipartite systems which have previously not been identified. In addition, our results can be implemented in today's experiments.
Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China

In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.
Artificial Potential Field with Discrete Map Transformation for Feasible Indoor Path Planning

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.
Sampling Overhead Analysis of Quantum Error Mitigation: Uncoded vs. Coded Systems

Quantum error mitigation (QEM) is a promising technique of protecting hybrid quantum-classical computation from decoherence, but it suffers from sampling overhead which erodes the computational speed. In this treatise, we provide a comprehensive analysis of the sampling overhead imposed by QEM. In particular, we show that Pauli errors incur the lowest sampling overhead among a large class of realistic quantum channels having the same average fidelity. Furthermore, we show that depolarizing errors incur the lowest sampling overhead among all kinds of Pauli errors. Additionally, we conceive a scheme amalgamating QEM with quantum channel coding, and analyse its sampling overhead reduction compared to pure QEM. Especially, we observe that there exist a critical number of gates contained in quantum circuits, beyond which their amalgamation is preferable to pure QEM.
Dipole-coupled emitters as deterministic entangled photon-pair sources

Scalable quantum systems require deterministic entangled photon pair sources. Here, we demonstrate a scheme that uses a dipole-coupled emitter pair to deterministically emit polarization-entangled photon pairs. Based on this scheme, we predict spectroscopic signatures and quantify the entanglement with realistic material parameters. In addition, we describe how the Bell state fidelity and efficiency can be optimized by precisely tuning transition frequencies. Finally, we discuss how defect emitters are natural candidates for the proposed scheme, offering numerous advantages including flexible on-chip photonic integration and tunable emission properties via external fields, electromagnetic environments, and defect selection.Received 16 June 2020Revised 23 September 2020Accepted 6 November 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043328Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasColor centersEntanglement productionAtomic, Molecular & OpticalCondensed Matter & Materials PhysicsQuantum Information
Coupling a Superconducting Qubit to a Left-Handed Metamaterial Resonator

Metamaterial resonant structures made from arrays of superconducting lumped-circuit elements can exhibit microwave-mode spectra with left-handed dispersion for the standing-wave resonances, resulting in a high density of modes in the same frequency range where superconducting qubits are typically operated, as well as a bandgap at lower frequencies that extends down to dc. Using this regime for multimode circuit quantum electrodynamics, we perform a series of measurements of such a superconducting metamaterial resonator coupled to a flux-tunable transmon qubit. Through microwave measurements of the metamaterial, we observe the coupling of the qubit to each of the modes that it passes through. Using a separate readout resonator, we probe the qubit dispersively and characterize the qubit energy relaxation as a function of frequency, which is strongly affected by the Purcell effect in the presence of the dense mode spectrum. Additionally, we investigate the ac Stark shift of the qubit as the photon number in the various metamaterial modes is varied. Through numerical simulations, we explore designs based on this scheme with enhanced coupling so that the coupling energy between the qubit and the metamaterial modes can exceed the intermode spacing in future devices with achievable circuit parameters. The ability to tailor the dense mode spectrum through the choice of circuit parameters and manipulate the photonic state of the metamaterial through interactions with qubits makes this a promising platform for analog quantum simulation with microwave photons and quantum memories.
Gesture Feature Extraction and Recognition Based on Image Processing

Gesture recognition has become increasingly popular, in response to the growing demand for intelligent and personalized human-computer interaction (HCI) and human-to-human interaction. However, gesture recognition raises a high requirement on the background color of the gesture image, and faces difficulty in extracting multiple gesture features. To solve these problems, this paper presents a novel approach for gesture feature extraction and recognition based on image processing. Firstly, the workflow of the proposed gesture recognition method was given, and a series of preprocessing was performed on the original gesture image, prior to formal extraction and recognition. Next, the authors detailed the extraction of features from gesture boundaries and fingertips. Finally, a convolutional neural network (CNN) was constructed for gesture recognition, and a gesture recognition model was developed based on residual network. The proposed approach was proved to be valid through experiments. The research results provide a reference for the application of CNN in the recognition of various postures or shapes.
Correlation-Aware Heuristic Search for Intelligent Virtual Machine Provisioning in Cloud Systems

The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs

Natural Language Inference (NLI) is a vital task in natural language processing.It aims to identify the logical relationship between two sentences.Most of the existing approaches make such inference based on semantic knowledge obtained through training corpus.The adoption of background knowledge is rarely seen or limited to a few specific types.In this paper, we propose a novel Knowledge Graph-enhanced NLI (KGNLI) model to leverage the usage of background knowledge stored in knowledge graphs in the field of NLI.KGNLI model consists of three components: a semantic-relation representation module, a knowledge-relation representation module, and a label prediction module.Different from previous methods, various kinds of background knowledge can be flexibly combined in the proposed KGNLI model.Experiments on four benchmarks, SNLI, MultiNLI, SciTail, and BNLI, validate the effectiveness of our model.
Flip Extended Visual Cryptography for Gray-Scale and Color Cover Images

Visual cryptography (VC) has found numerous applications in privacy protection, online transaction security, and voting security, etc. To counteract potential cheating attacks, Lin et al. proposed flip visual cryptography in 2010, where a second secret image can be revealed by stacking one share with a flipped version of another share. The second secret image can be designed as an additional verification mechanism. However, Lin’s scheme produces meaningless shares and is only applicable to binary secret images. It is interesting to explore whether it is possible to extend the flip VC to having cover images (i.e., extended VC) and these cover images are color images. This problem is challenging since too many restricting conditions need to be met. In this paper, we designed a flip VC for gray-scale and color cover images based on constraint error diffusion. We show that it is possible to meet all the constraints simultaneously. Compared with existing schemes, our scheme enjoys the following features: Color cover images, no computation needed for decoding, and no interference from cover image on the recovered secret image.
Detect All Abuse! Toward Universal Abusive Language Detection Models

Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years.Several previous works in online ALD focused on solving a single abusive language problem in a single domain, like Twitter, and have not been successfully transferable to the general ALD task or domain.In this paper, we introduce a new generic ALD framework, MACAS, which is capable of addressing several types of ALD tasks across different domains.Our generic framework covers multi-aspect abusive language embeddings that represent the target and content aspects of abusive language and applies a textual graph embedding that analyses the user's linguistic behaviour.Then, we propose and use the cross-attention gate flow mechanism to embrace multiple aspects of abusive language.Quantitative and qualitative evaluation results show that our ALD algorithm rivals or exceeds the six state-of-the-art ALD algorithms across seven ALD datasets covering multiple aspects of abusive language and different online community domains.