Reviewerly/exHarmony huggingface dataset:
A Fast Q-Learning Based Data Storage Optimization for Low Latency in Data Center Networks

Data storage optimizations (DS, e.g. low latency for data access) in data center networks(DCN) are difficult online-making problems. Previously, they are done with heuristics under static network models which highly rely on designers' understanding of the environment. Encouraged by recent successes in deep reinforcement learning techniques to solve intricate online assignment problems, we propose to use the Q-learning (QL) technique to train and learn from historical DS decisions, which can significantly reduce the data access delay. However, QL faces two challenges to be widely used in data centers. They are massive input data and the blindness on parameter settings which severely hamper the convergence of the learning process. To solve these two key problems, we develop an evolutionary QL scheme, named as LFDS (Low latency and Fast convergence Data Storage). In the initial stage of the LFDS, the input matrix of QL is sparse to shrink the dimensionality of the massive input data while retaining its information as much as possible. In the following training phase, a specialized neural network is adopted to achieves a quick approximation. To overcome the blindness during QL training, the two key parameters, learning rate, and discount rate are carefully tested with real data input and network architecture. The preferred range of learning rate and discount rate are recommended for the use of QL in data centers, which brings high training rewards and fast convergence. Extensive simulations with real-world data show that the data access latency is decreased by 23.5% and the convergence rate is increased by 15%.
Precise programmable quantum simulations with optical lattices

Abstract We present an efficient approach to precisely simulate tight binding models with optical lattices, based on programmable digital-micromirror-device (DMD) techniques. Our approach consists of a subroutine of Wegner-flow enabled precise extraction of a tight-binding model for a given optical potential, and a reverse engineering step of adjusting the potential for a targeting model, for both of which we develop classical algorithms to achieve high precision and high efficiency. With renormalization of Wannier functions and high band effects systematically calibrated in our protocol, we show the tight-binding models with programmable onsite energies and tunnelings can be precisely simulated with optical lattices integrated with the DMD techniques. With numerical simulation, we demonstrate that our approach would facilitate quantum simulation of localization physics with adequate programmability and atom-based boson sampling for illustration of quantum computational advantage. We expect this approach would pave a way towards large-scale and precise programmable quantum simulations based on optical lattices.
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation

We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, followed by a vanishing point estimation are very slow and often unreliable in realistic scenarios, we build on semantic segmentation of the input image. To obtain better segmentations, we introduce a robust, accurate and very efficient hypothesize-and-test scheme. The key idea is to use three segmentation hypotheses, each based on a different number of visible walls. For each hypothesis, we predict the image locations of the room corners and select the hypothesis for which the layout estimated from the room corners is consistent with the segmentation. We demonstrate the efficiency and robustness of our method on three challenging benchmark datasets, where we significantly outperform the state-of-the-art.
A Morphing Cargo Drone for Safe Flight in Proximity of Humans

Delivery drones used by logistics companies today are equipped with unshielded propellers, which represent a major hurdle for in-hand parcel delivery. The exposed propeller blades are hazardous to unsuspecting bystanders, pets, and untrained users. One solution to provide safety is to enclose a drone with an all-encompassing protective cage. However, the structures of existing cage designs have low density in order to minimize obstruction of propeller airflow, so as to not decrease efficiency. The relatively large openings in the cage do not protect hands and fingers from fast rotating propellers. Here we describe a novel approach to safety and aerodynamic efficiency by means of a high-density cage and morphing arms loosely inspired by the box turtle. The drone cage is made of a dense and lightweight grid. When flying in proximity of humans, the arms and propellers are retracted and fully sealed within the cage, thus making the drone safe and also reducing the total footprint. When flying at cruising altitude far from people and objects, the arms and propellers extend out of the protective grid, thus increasing aerodynamic efficiency by more than 20%.
No-reference stereoscopic image quality evaluator with segmented monocular features and perceptual binocular features

No-reference stereoscopic image quality assessment (NR SIQA) has aroused much attention in the multimedia application, but most current works cannot achieve reliable accuracy due to the non-visual meaning image patches and the weak biological visual models. Inspired by this, we propose a novel NR SIQA model by the introduction of the meaningful superpixel visual patches and the significant binocular perception models. Following the visual perceptual posterior probability theory, local monocular superpixel spatial entropy and the natural scene statistics (NSS) features are firstly utilized to present the high level semantic perceptual mechanism and image naturalness. Then the binocular biological models are applied to extract the binocular features to simulate the inner binocular interaction information, which well complement with the monocular features. Finally, a support vector regression (SVR) model is utilized to predict the objective quality score. Experiments are conducted on four popular stereoscopic image Databases, and the results demonstrate that the proposed model obtains a good consistency with the subjective scores, and has more reliable performance than previous works in terms of prediction accuracy and generalization capability.
FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try-On

Visual compatibility and virtual feel are critical metrics for fashion analysis yet are missing in existing fashion designs and platforms. An explicit model is much needed for implanting visual compatibility through fashion image inpainting and virtual try-on. With rapid advancements in the Computer Vision realm, the increase in creating customer experience which leads to the great potential of interest to retailers and customers. The public datasets available are very much fit for generating outfits from Generative Adversarial Networks (GANs) but the custom outfits of the users themselves lead to low accuracy levels. This work is the first step in analyzing and experimenting with the fit of custom outfits and visualizing it to the users on them which creates the great customer experience. The work analyses the need for providing visualization of custom outfits on users in the large corpora of AI in Fashion. The authors propose a novel architecture which facilitates the combining outfits provided by the retailers and visualize it on the users themselves using Neural Body Fit. This work creates a benchmark in disentangling the custom generation of cloth outfits using GANs and virtually trying it on the users to ensure a virtual-photorealistic appearance and results to create a great customer experience by using AI. Extensive experiments show the high accuracy levels on custom outfits generated by GANs but not in customized levels. This experiment creates new state-of-art results by plotting users pose for calculating the lengths of each body-part segment (hand, leg, and so forth), segmentation + NBF for accurate fitting of the cloth outfit. This paper is different from all other competitors in terms of approach for the virtual try-on for creating a new customer experience.
Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval

Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training. The assumption may not always be practical since the data of a few classes may be unavailable, or the classes may not appear at the time of training. Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) relaxes this constraint and allows the algorithm to handle previously unseen classes during the test. This paper proposes a generative approach based on the Stacked Adversarial Network (SAN) and the advantage of Siamese Network (SN) for ZS-SBIR. While SAN generates a high-quality sample, SN learns a better distance metric compared to that of the nearest neighbor search. The capability of the generative model to synthesize image features based on the sketch reduces the SBIR problem to that of an image-to-image retrieval problem. We evaluate the efficacy of our proposed approach on TU-Berlin, and Sketchy database in both standard ZSL and generalized ZSL setting. The proposed method yields a significant improvement in standard ZSL as well as in a more challenging generalized ZSL setting (GZSL) for SBIR.
Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification

The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
A Lightweight Short-Term Photovoltaic Power Prediction for Edge Computing

To meet the needs for energy savings in Internet of Things (IoT) systems, solar energy has been increasingly exploited to serve as a green and renewable source to allow systems to better operate in an energy-efficient way. In this respect, accurate PV power output prediction is a prerequisite for any energy saving scheme employed in these systems. In this work, we propose a unified training framework combined with the LightGBM algorithm to obtain a prediction model, which can provide short-term predictions of PV power output. Compared with the training in a single powerful machine, our proposed framework is more energy-efficient and fits into devices with limited computation and storage resources. The experimental results show that our proposed framework is superior to other benchmark machine learning algorithms.
Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers

The original fruit fly optimizer (FOA) has two core disadvantages: slow convergence speed and low solution quality. Furthermore, fruit fly optimizer tends to skip the optimal optimum when faced with complex or high-dimensional problems. To overcome these shortcomings, we introduce Gaussian mutation and orthogonal learning schemes into the fruit fly optimizer. On the one side, the orthogonal learning strategies can acquire more useful information during the exploratory and exploitative stages and build superior lead vectors. On the other hand, the Gaussian mutation mechanism also increases the population's perturbation and enhances the diversity of the swarm. With these mechanisms, the proposed method has a higher potential to avoid premature convergence and fall into local optimum. To validate the performance of the proposed method, it is compared with three other state-of-the-art variants of fruit fly optimizer over several reprentative benchmark functions. The results have demonstrated the efficacy of the proposed method is superior to the conventional fruit fly optimizer according to both convergence rapidity and solution quality. Simulations reveal that the proposed new FOA variant has more stable performance and high potential.
The Ifs and Buts of Less is More: A Serverless Computing Reality Check

Serverless computing defines a pay-as-you-go cloud execution model, where the unit of computation is a function that a cloud provider executes and auto-scales on behalf of a cloud consumer. Serverless suggests not (or less) caring about servers but focusing (more) on business logic expressed in functions. Server'less' may be `more' when getting developer expectations and platform propositions right and when engineering solutions that take specific behavior and constraints of (current) Function-as-a-Service platforms into account. To this end, in this invited paper, we present a summary of findings and lessons learned from a series of research experiments conducted over the past two years. We argue that careful attention must be placed on the promises associated with the serverless model, provide a reality-check for five common assumptions, and suggest ways to mitigate unwanted effects. Our findings focus on application workload distribution and computational processing complexity, the specific auto-scaling mechanisms in place, the behavior and strategies implemented with operational tasks, the constraints and limitations existing when composing functions, and the costs of executing functions.
A deep metric learning approach for histopathological image retrieval

To distinguish ambiguous images during specimen slides viewing, pathologists usually spend lots of time to seek guidance from confirmed similar images or cases, which is inefficient. Therefore, several histopathological image retrieval methods have been proposed for pathologists to easily obtain images sharing similar content with the query images. However, these methods cannot ensure a reasonable similarity metric, and some of them need lots of annotated images to train a feature extractor to represent images. Motivated by this circumstance, we propose the first deep metric learning-based histopathological image retrieval method in this paper and construct a deep neural network based on the mixed attention mechanism to learn an embedding function under the supervision of image category information. With the learned embedding function, original images are mapped into the predefined metric space where similar images from the same category are close to each other, so that the distance between image pairs in the metric space can be regarded as a reasonable metric for image similarity. We evaluate the proposed method on two histopathological image retrieval datasets: our self-established dataset and a public dataset called Kimia Path24, on which the proposed method achieves recall in top-1 recommendation (Recall@1) of 84.04% and 97.89% respectively. Moreover, further experiments confirm that the proposed method can achieve comparable performance to several published methods with less training data, which hedges the shortage of annotated medical image data to some extent. Code is available at https://github.com/easonyang1996/DML_HistoImgRetrieval.
Manipulating Complex Hybrid Entanglement and Testing Multipartite Bell Inequalities in a Superconducting Circuit

Quantum correlations in observables of multiple systems not only are of fundamental interest, but also play a key role in quantum information processing. As a signature of these correlations, the violation of Bell inequalities has not been demonstrated with multipartite hybrid entanglement involving both continuous and discrete variables. Here we create a five-partite entangled state with three superconducting transmon qubits and two photonic qubits, each encoded in the mesoscopic field of a microwave cavity. We reveal the quantum correlations among these distinct elements by joint Wigner tomography of the two cavity fields conditional on the detection of the qubits and by test of a five-partite Bell inequality. The measured Bell signal is 8.381±0.038, surpassing the bound of 8 for a four-partite entanglement imposed by quantum correlations by 10 standard deviations, demonstrating the genuine five-partite entanglement in a hybrid quantum system.
Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer

Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) image segmentation. In this paper, we propose a novel and intuitive framework to remove the appearance shift, and hence improve the generalization ability of DNNs. Our work has three highlights. First, we follow the spirit of universal style transfer to remove appearance shifts, which was not explored before for US images. Without sacrificing image structure details, it enables the arbitrary style-content transfer. Second, accelerated with Adaptive Instance Normalization block, our framework achieved real-time speed required in the clinical US scanning. Third, an efficient and effective style image selection strategy is proposed to ensure the target-style US image and testing content US image properly match each other. Experiments on two large US datasets demonstrate that our methods are superior to state-of-the-art methods on making DNNs robust against various appearance shifts.
Two-Pass Privacy Preserving Authenticated Key Agreement Scheme for Smart Grid

The design of an authenticated key agreement protocol for securing the smart meter communications in smart grid networks has attracted growing attention recently. Typically, realizing mutual authentication and key agreement without active involvement of a trusted third party is expected in this field. Thus, to achieve this aim, several authenticated key agreement schemes for smart grid have been presented; while as investigated in the following section of this work, most of these schemes remain vulnerable to various attacks. Therefore, to design a truly secure authenticated key agreement protocol for smart grid and make some contributions to this field, this work presents our new authenticated key agreement scheme for smart grid using Elliptic Curve Qu-Vanstone (ECQV) implicit certificate as the building block. Our scheme is pairing-free and just uses two passes to realize mutual authentication and key agreement as well as strong credential privacy, making it with high efficiency in both computation and communication overheads. Moreover, its security is formally proved under the widely accepted Canetti and Krawczyk (CK) security model, and is further confirmed by a heuristic security analysis.
Do You Feel Like Passing Through Walls?: Effect of Self-Avatar Appearance on Facilitating Realistic Behavior in Virtual Environments

Preventing users from walking through virtual boundaries (e.g., walls) is an important issue to be addressed in room-scale virtual environments (VEs), considering the safety and design limitations. Sensory feedback from wall collisions has been shown to be effective; however, it can disrupt the immersion. We assumed that a greater sense of presence would discourage users from walking through walls and conducted a two-factor between-subjects experiment (N = 92) that controls the anthropomorphism (realistic or abstract) and visibility (full-body or hand-only) of self-avatars. We analyzed the participants' behaviors and the moment they first penetrated the wall in game-like VEs that gradually instigated participants to penetrate the walls. The results showed that the realistic full-body self-avatar was the most effective for discouraging the participants from penetrating the walls. Furthermore, the participants with lower presence tended to walk through the walls sooner. This study can contribute to applications that require realistic user responses in VEs.
Connecting Distributed Families: Camera Work for Three-party Mobile Video Calls

Mobile video calling technologies have become a critical link to connect distributed families. However, these technologies have been principally designed for video calling between two parties, whereas family video calls involve young children often comprise three parties, namely a co-present adult (a parent or grandparent) helping with the interaction between the child and another remote adult. We examine how manipulation of phone cameras and management of co-present children is used to stage parent-child interactions. We present results from a video-ethnographic study based on 40 video recordings of video calls between 'left-behind' children and their migrant parents in China. Our analysis reveals a key practice of 'facilitation work', performed by grandparents, as a crucial feature of three-party calls. Facilitation work offers a new concept for HCI's broader conceptualisation of mobile video calling, suggesting revisions that design might take into consideration for triadic interactions in general.
Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment

Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.