Reviewerly/exHarmony huggingface dataset:
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit

In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution. We propose a novel degradation aware scene text recognizer with a pluggable super-resolution unit (PlugNet) to recognize low-quality scene text to solve this task from the feature-level. The whole networks can be trained end-to-end with a pluggable super-resolution unit (PSU) and the PSU will be removed after training so that it brings no extra computation. The PSU aims to obtain a more robust feature representation for recognizing low-quality text images. Moreover, to further improve the feature quality, we introduce two types of feature enhancement strategies: Feature Squeeze Module (FSM) which aims to reduce the loss of spatial acuity and Feature Enhance Module (FEM) which combines the feature maps from low to high to provide diversity semantics. As a consequence, the PlugNet achieves state-of-the-art performance on various widely used text recognition benchmarks like IIIT5K, SVT, SVTP, ICDAR15 and etc.
Heuristic initialization of PSO task scheduling algorithm in cloud computing

Task scheduling is one of the major issues in cloud computing environment. Efficient task scheduling is substantial to attain cost-effective execution and improve resource utilization. The task scheduling problem is classified to be a nondeterministic polynomial time (NP)-hard problem. This feature attracts researchers to utilize nature inspired metaheuristic algorithms. Initializing searching solutions randomly is one of the key features in such optimization algorithms. However, assisting metaheuristic algorithms with effective initialized solutions can significantly improve its performance. In this paper, an improved initialization of particle swarm optimization (PSO) using heuristic algorithms is proposed. Longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms are used to initialize the PSO. The performance of the proposed LJFP-PSO and MCT-PSO algorithms are evaluated in minimizing the makespan, total execution time, degree of imbalance, and total energy consumption metrices. Moreover, the performance of the proposed algorithms is compared with recent task scheduling methods. Simulation results revealed the effectiveness and superiority of the proposed LJFP-PSO and MCT-PSO compared to the conventional PSO and comparative algorithms.
Quantum concepts in optical polarization

We comprehensively review the quantum theory of the polarization properties of light. In classical optics, these traits are characterized by the Stokes parameters, which can be geometrically interpreted using the Poincaré sphere. Remarkably, these Stokes parameters can also be applied to the quantum world, but then important differences emerge: now, because fluctuations in the number of photons are unavoidable, one is forced to work in the three-dimensional Poincaré space that can be regarded as a set of nested spheres. Additionally, higher-order moments of the Stokes variables might play a substantial role for quantum states, which is not the case for most classical Gaussian states. This brings about important differences between these two worlds that we review in detail. In particular, the classical degree of polarization produces unsatisfactory results in the quantum domain. We compare alternative quantum degrees and put forth that they order various states differently. Finally, intrinsically nonclassical states are explored, and their potential applications in quantum technologies are discussed.
Coherent nonlinear optics of quantum emitters in nanophotonic waveguides

Abstract Coherent quantum optics, where the phase of a photon is not scrambled as it interacts with an emitter, lies at the heart of many quantum optical effects and emerging technologies. Solid-state emitters coupled to nanophotonic waveguides are a promising platform for quantum devices, as this element can be integrated into complex photonic chips. Yet, preserving the full coherence properties of the coupled emitter-waveguide system is challenging because of the complex and dynamic electromagnetic landscape found in the solid state. Here, we review progress toward coherent light-matter interactions with solid-state quantum emitters coupled to nanophotonic waveguides. We first lay down the theoretical foundation for coherent and nonlinear light-matter interactions of a two-level system in a quasi-one-dimensional system, and then benchmark experimental realizations. We discuss higher order nonlinearities that arise as a result of the addition of photons of different frequencies, more complex energy level schemes of the emitters, and the coupling of multiple emitters via a shared photonic mode. Throughout, we highlight protocols for applications and novel effects that are based on these coherent interactions, the steps taken toward their realization, and the challenges that remain to be overcome.
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

Active learning is an important technique for low-resource sequence labeling tasks.However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way of leveraging human annotations.We propose a simple but effective data augmentation method to improve label efficiency of active sequence labeling.Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration.The key difficulty is to generate plausible sequences along with token-level labels.In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples.Furthermore, we design a discriminator during sequence mixup, which judges whether the generated sequences are plausible or not.Our experiments on Named Entity Recognition and Event Detection tasks show that SeqMix can improve the standard active sequence labeling method by 2.27%-3.75% in terms of F 1 scores.The code and data for SeqMix can be found at https://github.com/rz-zhang/SeqMix.
The role of the project manager in agile software development projects

Agile teams are not meant to have project managers. Instead, agile methods such as Scrum and XP define roles such as product owner, scrum master, and coach. Studies have uncovered the existence of the project manager in agile projects, pointing to disconnect between theory and practice. To address this gap, a Grounded Theory study with a mixed methods approach was conducted using multiple sources of data including over 45 h of interviews with 39 software practitioners and quantitative data from 57 questionnaire respondents. We present and describe the project manager’s role in agile projects in terms of (a) everyday activities: facilitating, mentoring, negotiating, coordinating, and protecting, performed by the project manager using; (b) three management approaches: hard, moderate, and soft; (c) four traditional project management activities continued to be performed by them, including: tracking project progress, reporting on project status, budgeting and forecasting, and managing personnel; and (d) the influence of the presence of the project manager on the frequency with which agile activities are carried out by the teams. Our study highlights the continued presence of the role of the project manager in agile software projects as a part of the transition from traditional to agile ways of working.
Learning Object Relation Graph and Tentative Policy for Visual Navigation

Target-driven visual navigation aims at navigating an agent towards a given target based on the observation of the agent. In this task, it is critical to learn informative visual representation and robust navigation policy. Aiming to improve these two components, this paper proposes three complementary techniques, object relation graph (ORG), trial-driven imitation learning (IL), and a memory-augmented tentative policy network (TPN). ORG improves visual representation learning by integrating object relationships, including category closeness and spatial correlations, e.g., a TV usually co-occurs with a remote spatially. Both Trial-driven IL and TPN underlie robust navigation policy, instructing the agent to escape from deadlock states, such as looping or being stuck. Specifically, trial-driven IL is a type of supervision used in policy network training, while TPN, mimicking the IL supervision in unseen environment, is applied in testing. Experiment in the artificial environment AI2-Thor validates that each of the techniques is effective. When combined, the techniques bring significantly improvement over baseline methods in navigation effectiveness and efficiency in unseen environments. We report 22.8% and 23.5% increase in success rate and Success weighted by Path Length (SPL), respectively. The code is available at https://github.com/xiaobaishu0097/ECCV-VN.git .
Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop

We introduce an automatic, end-to-end method for recovering the 3D pose and shape of dogs from monocular internet images. The large variation in shape between dog breeds, significant occlusion and low quality of internet images makes this a challenging problem. We learn a richer prior over shapes than previous work, which helps regularize parameter estimation. We demonstrate results on the Stanford Dog Dataset, an ‘in the wild’ dataset of 20,580 dog images for which we have collected 2D joint and silhouette annotations to split for training and evaluation. In order to capture the large shape variety of dogs, we show that the natural variation in the 2D dataset is enough to learn a detailed 3D prior through expectation maximization (EM). As a by-product of training, we generate a new parameterized model (including limb scaling) SMBLD which we release alongside our new annotation dataset StanfordExtra to the research community.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases

When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack). The lack of robustness of DNNs against Trojan attacks could significantly harm real-life machine learning (ML) systems in downstream applications, therefore posing widespread concern to their trustworthiness. In this paper, we study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime, where only the weights of a trained DNN are accessed by the detector. We first propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection. We show that an effective data-limited TND can be established by exploring connections between Trojan attack and prediction-evasion adversarial attacks including per-sample attack as well as all-sample universal attack. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples. We show that such a TND can be built by leveraging the internal response of hidden neurons, which exhibits the Trojan behavior even at random noise inputs. The effectiveness of our proposals is evaluated by extensive experiments under different model architectures and datasets including CIFAR-10, GTSRB, and ImageNet.
Quantization Guided JPEG Artifact Correction

The JPEG image compression algorithm is the most popular method of image compression because of it's ability for large compression ratios. However, to achieve such high compression, information is lost. For aggressive quantization settings, this leads to a noticeable reduction in image quality. Artifact correction has been studied in the context of deep neural networks for some time, but the current methods delivering state-of-the-art results require a different model to be trained for each quality setting, greatly limiting their practical application. We solve this problem by creating a novel architecture which is parameterized by the JPEG file's quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings. ...
SPARK: Spatial-Aware Online Incremental Attack Against Visual Tracking

Adversarial attacks of deep neural networks have been intensively studied on image, audio, and natural language classification tasks. Nevertheless, as a typical while important real-world application, the adversarial attacks of online video tracking that traces an object's moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual tracking: online generating imperceptible perturbations that mislead trackers along with an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and C&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the spatial-aware online inc remental attac k (a.k.a. SPARK) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, SPARK quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than basic attacks. The in-depth evaluation of the state-of-the-art trackers (i.e., SiamRPN++ with AlexNet, MobileNetv2, and ResNet-50, and SiamDW) on OTB100, VOT2018, UAV123, and LaSOT demonstrates the effectiveness and transferability of SPARK in misleading the trackers under both UA and TA with minor perturbations.
PSC-Net: learning part spatial co-occurrence for occluded pedestrian detection

Detecting pedestrians, especially under heavy occlusion, is a challenging computer vision problem with numerous real-world applications. This paper introduces a novel approach, termed as PSC-Net, for occluded pedestrian detection. The proposed PSC-Net contains a dedicated module that is designed to explicitly capture both inter and intra-part co-occurrence information of different pedestrian body parts through a graph convolutional network (GCN). Both inter and intra-part co-occurrence information contribute towards improving the feature representation for handling varying level of occlusions, ranging from partial to severe occlusions. Our PSC-Net exploits the topological structure of pedestrian and does not require part-based annotations or additional visible bounding-box (VBB) information to learn part spatial co-occurrence. Comprehensive experiments are performed on three challenging datasets: CityPersons, Caltech, and CrowdHuman datasets. Particularly, in terms of log-average miss rates and with the same backbone and input scale as those of the state-of-the-art MGAN, the proposed PSC-Net achieves absolute gains of 4.0% and 3.4% over MGAN on the heavy occlusion subsets of CityPersons and Caltech test sets, respectively.
Entanglement Distribution in a Quantum Network: A Multicommodity Flow-Based Approach

We consider the problem of optimising the achievable EPR-pair distribution rate between multiple source-destination pairs in a quantum internet, where the repeaters may perform a probabilistic bell-state measurement and we may impose a minimum end-to-end fidelity as a requirement. We construct an efficient linear programming formulation that computes the maximum total achievable entanglement distribution rate, satisfying the end-to-end fidelity constraint in polynomial time (in the number of nodes in the network). We also propose an efficient algorithm that takes the output of the linear programming solver as an input and runs in polynomial time (in the number of nodes) to produce the set of paths to be used to achieve the entanglement distribution rate. Moreover, we point out a practical entanglement generation protocol which can achieve those rates.
The effect of Google Expeditions virtual reality on EFL learners’ willingness to communicate and oral proficiency

This study reports the results of a mixed-methods investigation on the impact of a virtual reality (VR) tool on Iranian EFL learners' willingness to communicate (WTC) and oral proficiency. Twenty upper-intermediate EFL learners were divided into experimental and control groups role-playing as museum guides with and without Google Expeditions VR tool, respectively. Data collection comprised the participants' video-recorded oral performance, willingness to communicate scale, and focus group semi-structured interviews. Independent and paired sample t-test measures were used to investigate the groups' differences in terms of their WTC and oral proficiency development. Thematic analysis was also adopted to explore the participants' attitudes and perceptions toward the use of Google Expeditions in language classrooms. Quantitative findings showed that the experimental group significantly outperformed the control group in terms of oral performance and were more willing to communicate at the end of the experiment. The thematic analysis of the qualitative data indicated that the VR tool enhanced the participants' general knowledge, motivation, enthusiasm, cultural awareness, and confidence.
Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.
Blockchain and Federated Learning for 5G Beyond

In 5G and beyond networks, the increasing inclusion of heterogeneous smart devices and the rising privacy and security concerns, are two crucial challenges in terms of computation complexity and privacy preservation for Artificial Intelligence (AI)-based solutions. In this regard, federated learning emerges as a new technique, which enlarges the scale of training data, and protects the privacy of user data. The development of edge computing makes it possible to apply federated learning to beyond 5G. However, the security of local parameters, the learning quality, and the varying computing and communication resources, are crucial issues that remain unexplored in federated learning schemes. In this article, we propose a block-chain empowered federated learning framework, and present its potential application scenarios in beyond 5G. We enhance the security and privacy by integrating blockchain into a federated learning scheme for maintaining the trained parameters. In particular, we formulate the resource sharing task as a combinational optimization problem while taking resource consumption and learning quality into account. We design a deep reinforcement learning based algorithm to find an optimal solution to the problem. Numerical results show that the proposed scheme achieves high accuracy and good convergence.
Applications and Challenges in Video Surveillance via Drone: A Brief Survey

Our society is moving quickly towards smart homes and smart cities, requiring more and more deployment of Internet of Things (IoT) devices. To achieve smart urbanization, continuous surveillance is necessary. Research about video surveillance via Closed-Circuit Television (CCTV) cameras is in play for decades but poses different problems, i.e., limited area coverage, no location sharing, and tracking capabilities. On the other hand, vision sensors mounted on drones are more scalable and flexible with more comprehensive surveillance coverage. But at the same time, drones also encounter several challenges, such as limited processing and power resources, trembling camera effects in the video feed, disturbance in transmission signals, etc. Drones surveillance lacks the researcher's attentiveness, and therefore, we collect the related literature and confer their practical viewpoint broadly. This article focuses on video surveillance using drones in object detection and tracking, video summarization, persistent monitoring of the target, search and rescue operation in a hostile environment, traffic management in smart cities, and disaster management in an apocalyptic situation. This brief survey sheds light on the research gaps and profound insights of the methods used in the mentioned articles by opening up future research tracks for the Computer Vision (CV) enthusiasts using Unmanned Aerial Vehicles (UAV).
Word–Sentence Framework for Remote Sensing Image Captioning

Remote sensing image captioning (RSIC), which aims at generating a well-formed sentence for a remote sensing image, has attracted more attention in recent years. The general framework for RSIC is the encoder–decoder architecture containing two submodels of encoder and decoder. Although the significant performance is obtained, the encoder–decoder architecture is a black-box model with a lack of explainability. To overcome this drawback, in this article, we propose a new explainable word–sentence framework for RSIC. The proposed word–sentence framework consists of two parts: word extractor and sentence generator, where the former extracts the valuable words in the given remote sensing image, while the latter organizes these words into a well-formed sentence. The proposed framework decomposes RSIC into a word classification task and a word sorting task, which is more in line with human intuitive understanding. On the basis of the word–sentence framework, some ablation experiments are conducted on the three public RSIC data sets of Sydney-captions, UCM-captions, and RSICD to explore the specific and effective network structures. In order to evaluate the proposed word–sentence framework objectively, we further conduct some comparative experiments on these three data sets and achieve comparable results in comparison with the encoder–decoder-based methods.