Reviewerly/exHarmony huggingface dataset:
The orienteering problem: a hybrid control formulation

In the last years, a growing number of challenging applications in navigation, logistics, and tourism were modeled as orienteering problems. This problem has been proposed in relation to a sport race where certain control points must be visited in a minimal time. In a certain kind of these competitions, the choice of the number and the order for the control points are left to the competitor. We propose an original approach to solve the orienteering problem based on hybrid control. The continuous state of the system contains information about the navigation toward the next control point. In contrast, the discrete state keeps track of the already visited ones. The control problem is solved using non-standard dynamical programming techniques.
Mixed-integer optimization-based planning for autonomous racing with obstacles and rewards

Trajectory planning with the consideration of obstacles is a classical task in autonomous driving and robotics applications. This paper introduces a novel solution approach for the subclass of autonomous racing problems which is additionally capable of dealing with reward objects. This special type of objects is representing particular regions in state space, whose optional reaching is somehow beneficial (e.g. results in bonus points during a race). First, a homotopy class is selected which represents the left/right and catch/ignore decisions related to obstacle avoidance and reward collection, respectively. For this purpose, a linear mixed-integer problem is posed such that an approximated combinatorial problem is solved and repetitive switching decisions between solver calls are avoided. Secondly, an optimal control problem (OCP) based on a single-track vehicle model is solved within this homotopy class. In the corresponding nonlinear program, homotopy iterations are performed on the race track boundaries which correspond to the previously chosen homotopy class. This leads to an improved convergence of the solver compared to the direct approach. The mixed-integer method’s effectiveness is demonstrated within a real-world test scenario during the autonomous racing competition Roborace. Furthermore, its combination with the OCP as well as the performance gain resulting from the homotopy iterations are shown in simulation.
Grounding Consistency: Distilling Spatial Common Sense for Precise Visual Relationship Detection

Scene Graph Generators (SGGs) are models that, given an image, build a directed graph where each edge represents a predicted subject predicate object triplet. Most SGGs silently exploit datasets' bias on relationships' context, i.e. its subject and object, to improve recall and neglect spatial and visual evidence, e.g. having seen a glut of data for person wearing shirt, they are overconfident that every person is wearing every shirt. Such imprecise predictions are mainly ascribed to the lack of negative examples for most relationships, which obstructs models from meaningfully learning predicates, even those that have ample positive examples. We first present an indepth investigation of the context bias issue to showcase that all examined state-of-the-art SGGs share the above vulnerabilities. In response, we propose a semi-supervised scheme that forces predicted triplets to be grounded consistently back to the image, in a closed-loop manner. The developed spatial common sense can be then distilled to a student SGG and substantially enhance its spatial reasoning ability. This Grounding Consistency Distillation (GCD) approach is model-agnostic and benefits from the superfluous unlabeled samples to retain the valuable context information and avert memorization of annotations. Furthermore, we demonstrate that current metrics disregard unlabeled samples, rendering themselves incapable of reflecting context bias, then we mine and incorporate during evaluation hard-negatives to reformulate precision as a reliable metric. Extensive experimental comparisons exhibit large quantitative - up to 70% relative precision boost on VG200 dataset - and qualitative improvements to prove the significance of our GCD method and our metrics towards refocusing graph generation as a core aspect of scene understanding. Code available at https://github.com/deeplab-ai/grounding-consistent-vrd.
A Novel Simulation-Reality Closed-Loop Learning Framework for Autonomous Robot Skill Learning

In recent years, data-driven learning methods have been widely studied for autonomous robot skill learning. However, these methods rely on large amounts of robot–environment interaction data for training, which largely prevents them from being applied to real-world robots. To address this problem, this article proposes a novel simulation-reality closed-loop learning framework for autonomous robot skill learning that can improve data efficiency, enhance policy stability, and achieve effective policy simulation-to-reality (sim2real) transfer. First, a hybrid control model combining the asymmetric deep deterministic policy gradients (Asym-DDPGs) model and the forward prediction control (FPC) model is proposed to learn vision-based manipulation policies in simulations, which can decompose complex tasks to improve learning efficiency. Second, a novel pixel-level domain adaptation method named Position-CycleGAN is designed to translate real images to simulated images while also preserving the task-related information. The policy trained in simulations can be directly migrated into real robots in a reverse reality-to-simulation manner using the Position-CycleGAN model. The experimental results validate the effectiveness of the proposed framework. This work provides an efficient and feasible path for achieving autonomous skill learning.
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling

High-fidelity face digitization solutions often combine multi-view stereo (MVS) techniques for 3D reconstruction and a non-rigid registration step to establish dense correspondence across identities and expressions. A common problem is the need for manual clean-up after the MVS step, as 3D scans are typically affected by noise and outliers and contain hairy surface regions that need to be cleaned up by artists. Furthermore, mesh registration tends to fail for extreme facial expressions. Most learning-based methods use an underlying 3D morphable model (3DMM) to ensure robustness, but this limits the output accuracy for extreme facial expressions. In addition, the global bottleneck of regression architectures cannot produce meshes that tightly fit the ground truth surfaces. We propose ToFu, Topological consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM. Our novel progressive mesh generation network embeds the topological structure of the face in a feature volume, sampled from geometry-aware local features. A coarse-to-fine architecture facilitates dense and accurate facial mesh predictions in a consistent mesh topology. ToFu further captures displacement maps for pore-level geometric details and facilitates high-quality rendering in the form of albedo and specular reflectance maps. These high-quality assets are readily usable by production studios for avatar creation, animation and physically-based skin rendering. We demonstrate state-of-the-art geometric and correspondence accuracy, while only taking 0.385 seconds to compute a mesh with 10K vertices, which is three orders of magnitude faster than traditional techniques. The code and the model are available for research purposes at https://tianyeli.github.io/tofu.
Sequen-C: A Multilevel Overview of Temporal Event Sequences

Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build a multilevel overview of event sequences, whose granularity can be transformed across sequence clusters (vertical level-of-detail) or longitudinally (horizontal level-of-detail), using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric - then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III; which demonstrate how the technique can aid users to obtain a summary of common and deviating pathways, and explore data attributes for selected patterns.
Adaptive and fast image superpixel segmentation approach

Superpixel is one of the most popular image over-segmentations with broad applications in the computer vision field to reduce their computations by replacing pixels as primitives. The main concerns of one superpixel generation algorithm are its accuracy and efficiency. One of the most important things in superpixel accuracy is to fit the image boundaries tightly with a few pixels as possible (namely minimal contour density, which is measured by the percent of superpixel contour pixels in the whole image). In this paper, we propose a new fast algorithm based on the clustering method to produce superpixels accurately with low contour density. First, we adopt the linear path from a pixel to one superpixel seed to define a regular term and propose a new distance measurement between them. In addition, we introduce the gradient and Local Binary Pattern (LBP) features and propose formulas of parameters in the proposed method adaptively. In this way, we can use the new distance measurement to group pixels as initial regions adaptively and produce the final superpixels by merging those small ones. Finally, we test the new algorithm on two public datasets and compare it with the state-of-the-art. Our method can generate superpixels with lower contour density while being competitive in accuracy and computational time.
Resume Parsing based on Multi-label Classification using Neural Network models

Application for jobs usually brings much work for both appliers and HR. Appliers want to apply for the jobs which they are most suitable. The number of applications for a particular position can be significant, making the candidates’ selection cumbersome for HR. Nowadays, hiring processes are often conducted through the Virtual mode with emails. This creates chances for analyzing the data in the resume. Therefore, to enhance selection problems’ efficiency, resume parsing algorithms have been developed in recent years to predict resume-based skills or good jobs quickly. The artificial neural network is a hot spot in the field of artificial intelligence since the 1980s. It abstracts the human brain's neural network from the angle of information processing, establishes some simple models, and forms different networks according to different connection modes. In recent years, neural networks-based algorithms perform high efficiency in processing text classification. This paper put forward some of the efficient algorithms used in text classification, Like BPNN, CNN, BiLSTM, and CRNN, for resume parsing. The original resumes are parsed by splitting them into words, and word base is trained to get the most appropriate word, which has a high score in the resume is resulting suitable job for each resume. The CRNN performs best in resume parsing, which the accuracy can reach 96%. CNN places the lowest accuracy. The BPNN achieves good accuracy but brings inflexible.
Multi-criteria Web Services Selection: Balancing the Quality of Design and Quality of Service

Web service composition allows developers to create applications via reusing available services that are interoperable to each other. The process of selecting relevant Web services for a composite service satisfying the developer requirements is commonly acknowledged to be hard and challenging, especially with the exponentially increasing number of available Web services on the Internet. The majority of existing approaches on Web Services Selection are merely based on the Quality of Service (QoS) as a basic criterion to guide the selection process. However, existing approaches tend to ignore the service design quality, which plays a crucial role in discovering, understanding, and reusing service functionalities. Indeed, poorly designed Web service interfaces result in service anti-patterns, which are symptoms of bad design and implementation practices. The existence of anti-pattern instances in Web service interfaces typically complicates their reuse in real-world service-based systems and may lead to several maintenance and evolution problems. To address this issue, we introduce a new approach based on the Multi-Objective and Optimization on the basis of Ratio Analysis method (MOORA) as a multi-criteria decision making (MCDM) method to select Web services based on a combination of their (1) QoS attributes and (2) QoS design. The proposed approach aims to help developers to maintain the soundness and quality of their service composite development processes. We conduct a quantitative and qualitative empirical study to evaluate our approach on a Quality of Web Service dataset. We compare our MOORA-based approach against four commonly used MCDM methods as well as a recent state-of-the-art Web service selection approach. The obtained results show that our approach outperforms state-of-the-art approaches by significantly improving the service selection quality of top- k selected services while providing the best trade-off between both service design quality and desired QoS values. Furthermore, we conducted a qualitative evaluation with developers. The obtained results provide evidence that our approach generates a good trade-off for what developers need regarding both QoS and quality of design. Our selection approach was evaluated as “relevant” from developers point of view, in improving the service selection task with an average score of 3.93, compared to an average of 2.62 for the traditional QoS-based approach.
Morphometric measurements of blood cell

Measurement of morphometric parameters of blood cells play a key role in hematological examination representing one of the main tools for the diagnosis and evaluation of blood disorders. Different techniques are currently used to measure blood cell parameters to provide important clinical information, to consolidate pertinent second opinions, and to minimize the human intervention in the diagnosis process. This paper presents a brief overview of the research work that can be found in the literature dealing with the measurement methods for morphometry of blood cells. The main challenges and limitations of the presented methods from the metrological point of view are discussed too.
A Trustworthy Framework of Artificial Intelligence for Power Grid Dispatching Systems

With the widespread application of artificial intelligence (AI) technologies in power systems, the properties of lack of reliability and transparency for AI technologies have revealed gradually. Here, how to build a trustworthy-AI framework based on the power system is the focus. Due to the multidimensional and heterogeneous information of power grid data, the heterogeneous graph attention network (HGAT) model of power grid dispatching is established, and the corresponding explainer (HGAT-Explainer) for the model of power equipment faults is proposed to provide more favorable support for the trustworthy-AI systems.
Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation

The image-to-image translation (I2TT) model takes a target label or a reference image as the input, and changes a source into the specified target domain. The two types of synthesis, either label- or reference-based, have substantial differences. Particularly, the label-based synthesis reflects the common characteristics of the target domain, and the reference-based shows the specific style similar to the reference. This paper intends to bridge the gap between them in the task of multi-attribute I2TT. We design the label- and reference-based encoding modules (LEM and REM) to compare the domain differences. They first transfer the source image and target label (or reference) into a common embedding space, by providing the opposite directions through the attribute difference vector. Then the two embeddings are simply fused together to form the latent code S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rand</inf> (or S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ref</inf> ), reflecting the domain style differences, which is injected into each layer of the generator by SPADE. To link LEM and REM, so that two types of results benefit each other, we encourage the two latent codes to be close, and set up the cycle consistency between the forward and backward translations on them. Moreover, the interpolation between the S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rand</inf> and S <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ref</inf> is also used to synthesize an extra image. Experiments show that label- and reference-based synthesis are indeed mutually promoted, so that we can have the diverse results from LEM, and high quality results with the similar style of the reference. Code will be available at https://github.com/huangqiusheng/BridgeGAN.
Conditional Training with Bounding Map for Universal Lesion Detection

Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis. Promising ULD results have been reported by coarse-to-fine two-stage detection approaches, but such two-stage ULD methods still suffer from issues like imbalance of positive v.s. negative anchors during object proposal and insufficient supervision problem during localization regression and classification of the region of interest (RoI) proposals. While leveraging pseudo segmentation masks such as bounding map (BM) can reduce the above issues to some degree, it is still an open problem to effectively handle the diverse lesion shapes and sizes in ULD. In this paper we propose a BM-based conditional training for two-stage ULD, which can (i) reduce positive vs. negative anchor imbalance via a BM-based conditioning (BMC) mechanism for anchor sampling instead of traditional IoU-based rule; and (ii) adaptively compute size-adaptive BM (ABM) from lesion bounding-box, which is used for improving lesion localization accuracy via ABM-supervised segmentation. Experiments with four state-of-the-art methods show that the proposed approach can bring an almost free detection accuracy improvement without requiring expensive lesion mask annotations.
Real-time Vanishing Point Detector Integrating Under-parameterized RANSAC and Hough Transform

We propose a novel approach that integrates underparameterized RANSAC (UPRANSAC) with Hough Transform to detect vanishing points (VPs) from un-calibrated monocular images. In our algorithm, the UPRANSAC chooses one hypothetical inlier in a sample set to find a portion of the VP’s degrees of freedom, which is followed by a highly reliable brute-force voting scheme (1-D Hough Transform) to find the VP’s remaining degrees of freedom along the extension line of the hypothetical inlier. Our approach is able to sequentially find a series of VPs by repeatedly removing inliers of any detected VPs from minimal sample sets until the stop criterion is reached. Compared to traditional RANSAC that selects 2 edges as a hypothetical inlier pair to fit a model of VP hypothesis and requires hitting a pair of inliners, the UPRANSAC has a higher likelihood to hit one inliner and is more reliable in VP detection. Meanwhile, the tremendously scaled-down voting space with the requirement of only 1 parameter for processing significantly increased the performance efficiency of Hough Transform in our scheme. Testing results with well-known benchmark datasets show that the detection accuracies of our approach were higher or on par with the SOTA while running in deeply real-time zone.
Multiple-qubit Rydberg quantum logic gate via dressed-state scheme

We present a scheme to realize multiple-qubit quantum state transfer and quantum logic gate by combining the advantages of Vitanov-style pulses and dressed-state-based shortcut to adiabaticity (STA) in Rydberg atoms. The robustness of the scheme to spontaneous emission can be achieved by reducing the population of Rydberg excited states through STA technology. Meanwhile, the control errors can be minimized through using the well-designed pulses. Moreover, the dressed-state method applied in the scheme makes the quantum state transfer more smoothly turned on or off with high fidelity and also faster than traditional shortcut to adiabaticity methods. By using Rydberg antiblockade (RAB) effect (Su et al., 2020 [1]), the multiple-qubit Toffoli gate can be constructed under a general selection conditions of the parameters.
Security and Privacy Smart Contract Architecture for Energy Trading based on Blockchains

Nowaday, centralized smart grid systems encounter many challenges to peer-to-peer (P2P) energy trading, such as communications overhead, security, and privacy issues. Blockchain-based energy trading has been proposed as a possible solution to the above problems. This paper proposes a secure and automated blockchain-based framework that allows energy producers and consumers to conduct energy trade without intermediate entity interaction. Smart contracts have been established to automate the energy trade based on an agreement energy-relevant both from the supply and demand sides without third parties. In a smart contract, if all transactions are successful, the energy trade will take place. We used the Solidity programming language and Metamask wallet to create the smart contract. Then, smart contract implementation results in the Ropsten blockchain network are tested and compared with related works. According to the analysis, the proposed framework has enhanced security and privacy.
Self-supervised Learning of Inter-label Geometric Relationships for Gleason Grade Segmentation

Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. Manual segmentation maps from the training set are used to train a Shape Restoration Network (ShaRe-Net) that predicts missing mask segments in a self-supervised manner. Using DenseUNet as the backbone generator architecture we incorporate latent variable sampling to inject diversity in the image generation process and thus improve robustness. Experimental results demonstrate the superiority of our method over competing image synthesis methods for segmentation tasks. Ablation studies show the benefits of integrating geometry and diversity in generating high-quality images. Our self-supervised approach with limited class-labeled data achieves better performance than fully supervised learning.