Reviewerly/exHarmony huggingface dataset:
Tangible futures: Combining scenario thinking and personas - A pilot study on urban mobility

Scenario planning methods tend to work at an aggregate level and to consider homogeneous populations, thus levelling the variable effects of future developments on different social groups. Decision-making based on such scenarios bears the risk of missing undesired impacts on specific groups of people, which may cause social tensions or require costly countermeasures. New approaches are needed to provide a better basis for socially responsible planning by making clearer the complex social impacts of future scenarios. In marketing and user-centered design, persona models are developed to represent typologies of users to cover a broad range of needs and requirements. We propose a systematic method, called the Scenario Personarrative method, for combining scenario thinking and personas into a structured but nonetheless flexible process, which allows generating fine-grained individualized narratives. We describe a pilot application in a case study on urban mobility. This pilot used existing scenarios and focused on a two-hour workshop where twelve experts created personas and the associated narratives across three scenarios of urban mobility in 2030. This pilot shows the applicability of the method for making potential effects on different social groups more tangible. We propose ways forward for further evaluation of the proposed methodology.
Weighted voting on the blockchain: Improving consensus in proof of stake protocols

Summary Proof of stake (PoS) protocols rely on voting mechanisms to reach consensus on the current state. If an enhanced majority of staking nodes, also called validators , agree on a proposed block, then this block is appended to the blockchain. Yet these protocols remain vulnerable to faults caused by validators who abstain either accidentally or maliciously. To protect against such faults while retaining the PoS selection and reward allocation schemes, we study weighted voting in validator committees. We formalize the block creation process and introduce validators' voting profiles which we update by a multiplicative weights algorithm relative to validators' voting behavior and aggregate blockchain rewards. Using this framework, we leverage weighted majority voting rules that optimize collective decision making to show, both numerically and analytically, that the consensus mechanism is more robust if validators' votes are appropriately scaled. We raise potential issues and limitations of weighted voting in trustless, decentralized networks and relate our results to the design of current PoS protocols.
Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people's daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety.Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments.Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable.Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning.Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established.Based on the A * algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm.Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the explorationexploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs.The global-local hybrid UAV path planning algorithm was formed by combining the two.The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.
Tree-Structured Kronecker Convolutional Network for Semantic Segmentation

Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect partial information. To tackle this issue, we firstly propose a novel Kronecker convolution which adopts Kronecker product to expand the standard convolutional kernel for taking into account the partial feature neglected by atrous convolutions. Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters. Secondly, we propose a Tree-structured Feature Aggregation (TFA) module which follows a recursive rule to expand and forms a hierarchical structure. Thus, it can naturally learn representations of multi-scale objects and encode hierarchical contextual information in complex scenes. Finally, we design a Tree-structured Kronecker Convolutional Network (TKCN) which employs Kronecker convolution and TFA module. Extensive experiments on three datasets, PAS-CAL VOC 2012, PASCAL-Context and Cityscapes, verify the effectiveness of our proposed approach.
Polishing Decision-Based Adversarial Noise With a Customized Sampling

As an effective black-box adversarial attack, decision-based methods polish adversarial noise by querying the target model. Among them, boundary attack is widely applied due to its powerful noise compression capability, especially when combined with transfer-based methods. Boundary attack splits the noise compression into several independent sampling processes, repeating each query with a constant sampling setting. In this paper, we demonstrate the advantage of using current noise and historical queries to customize the variance and mean of sampling in boundary attack to polish adversarial noise. We further reveal the relationship between the initial noise and the compressed noise in boundary attack. We propose Customized Adversarial Boundary (CAB) attack that uses the current noise to model the sensitivity of each pixel and polish adversarial noise of each image with a customized sampling setting. On the one hand, CAB uses current noise as a prior belief to customize the multivariate normal distribution. On the other hand, CAB keeps the new samplings away from historical failed queries to avoid similar mistakes. Experimental results measured on several image classification datasets emphasizes the validity of our method.
Passive and active nonlocality sharing for a two-qubit system via weak measurements

A well-known property of quantum nonlocality is monogamy. However, recent research by Silva et al. shows that multiple observers can share the nonlocality by using weak measurements [Phys. Rev. Lett. 114, 250401 (2015)]. In this paper, we deeply discuss nonlocality sharing for an arbitrary two-qubit state via weak measurements. Two types of nonlocality sharing, passive nonlocality sharing and active nonlocality sharing, which is another type of double violation of Clauser-Horne-Shimony-Holt (CHSH) inequality with many counterintuitive results existing, are fully analyzed. The two types are quite distinct. Active nonlocality sharing can be observed when Bob1 performs strong measurements only except for the ideal strong measurements. Also, a double violation still can be observed in the active nonlocality sharing even using a square pointer type for weak measurements of Bob1, which is impossible in the passive one. As a special example, for a Bell state, a double violation of Clauser-Horne-Shimony-Holt inequality can be always observed in a broad range whether the weak measurement of Bob1 is with an optimal pointer or a square pointer.
An instance voting approach to feature selection

In this work, we address the problem of supervised feature selection (FS) for high-dimensional datasets with a small number of instances. Here, we propose a novel heuristic FS approach, Conditional Priority Coverage Maximization (CPCM) which seeks to leverage the local information provided by the small set of instances. We define the vote assigned by an instance to a feature as the local relevance of the latter. Also, we show that the proposed voting scheme is asymptotically related to the Bayes’ decision rule for minimum risk classification. Next, we exploit the instance votes for feature selection by posing it as a set-covering problem — we seek to select a subset of features such that they can together cover the instances. This approach avoids the selection of redundant features, while selecting relevant ones. In addition, we formulate the stopping criterion to select a compact subset of features. Through experiments on synthetic and real datasets, we demonstrated that CPCM outperforms other graph based FS techniques and state-of-the-art FS approaches employing mutual information (MI). Further, we evaluated the stability of CPCM to minor variations in the training data and found it to be reasonably robust.
Side Information for Face Completion: A Robust PCA Approach

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and eight real-world datasets to verify their effectiveness.
Diagnosis of Asthma Based on Routine Blood Biomarkers Using Machine Learning

Intelligent medical diagnosis has become common in the era of big data, although this technique has been applied to asthma only in limited contexts. Using routine blood biomarkers to identify asthma patients would make clinical diagnosis easier to implement and would enhance research of key asthma variables through data mining techniques. We used routine blood data from healthy individuals to construct a Mahalanobis space (MS). Then, we calculated Mahalanobis distances of the training routine blood data from 355 asthma patients and 1,480 healthy individuals to ensure the efficiency of MS. Orthogonal arrays and signal-to-noise ratios were used to optimize blood biomarker variables. Receiver operating characteristic (ROC) curve was used to determine the threshold value. Ultimately, we validated the system on 182 individuals based on the threshold value. Out of 35 patients with asthma, MTS correctly classified 94.15% of patients. In addition, 97.20% of 147 healthy individuals were correctly classified. The system isolated 7 routine blood biomarkers. Among these biomarkers, platelet distribution width, mean platelet volume, white blood cell count, eosinophil count, and lymphocyte ratio performed well in asthma diagnosis. In brief, MTS shows promise as an accurate method to identify asthma patients based on 7 vital blood biomarker variables and threshold determined by the ROC curve, thus offering the potential to simplify diagnostic complexity and optimize clinical efficiency.
ScenarioSA: A Dyadic Conversational Database for Interactive Sentiment Analysis

Interactive sentiment analysis is an emerging, yet challenging, subtask of the natural language processing problem.It aims to discover the affective state and sentimental change of each person in a conversation, and has attracted an increasing attention from both academia and industry.Existing sentiment analysis approaches are insufficient in modelling the interactions among people.However, the development of new approaches are critically limited by the lack of labelled interactive sentiment datasets.In this paper, we present a new conversational database that we have created and made publicly available, namely ScenarioSA, for interactive sentiment analysis.We manually label 2,214 multi-turn English conversations collected from various websites that provide online communication services.In comparison with existing sentiment datasets, ScenarioSA (1) is no longer limited to one specific domain but covers a wide range of topics and scenarios; (2) describes the interactions between two speakers of each conversation; and (3) reflects the sentimental evolution of each speaker over the course of a conversation.Finally, we propose an extension of interactive attention networks that could model the interactions, and compare various strong sentiment analysis algorithms on ScenarioSA, demonstrating the need of novel interactive sentiment analysis models and the potential of ScenarioSA to facilitate the development of such models.
Improved YOLO v5 with balanced feature pyramid and attention module for traffic sign detection

With the development of automatic driving technology, traffic sign detection has become a very important task. However, it is a challenging task because of the complex traffic sign scene and the small size of the target. In recent years, a number of convolutional neural network (CNN) based object detection methods have brought great progress to traffic sign detection. Considering the still high false detection rate, as well as the high time overhead and computational overhead, the effect is not satisfactory. Therefore, we employ lightweight network model YOLO v5 (You Only Look Once) as our work foundation. In this paper, we propose an improved YOLO v5 method by using balances feature pyramid structure and global context block to enhance the ability of feature fusion and feature extraction. To verify our proposed method, we have conducted a lot of comparative experiments on the challenging dataset Tsinghua-Tencent-100K (TT100K). The experimental results demonstrate that the mAP@.5 and mAP@.5:0.95 are improved by 1.9% and 2.1%, respectively.
Robust Ellipse Fitting With Laplacian Kernel Based Maximum Correntropy Criterion

The performance of ellipse fitting may significantly degrade in the presence of outliers, which can be caused by occlusion of the object, mirror reflection or other objects in the process of edge detection. In this paper, we propose an ellipse fitting method that is robust against the outliers, and thus maintaining stable performance when outliers can be present. We formulate an optimization problem for ellipse fitting based on the maximum entropy criterion (MCC), having the Laplacian as the kernel function from the well-known fact that the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm error measure is robust to outliers. The optimization problem is highly nonlinear and non-convex, and thus is very difficult to solve. To handle this difficulty, we divide it into two subproblems and solve the two subproblems in an alternate manner through iterations. The first subproblem has a closed-form solution and the second one is cast as a convex second-order cone program (SOCP) that can reach the global solution. By so doing, the alternate iterations always converge to an optimal solution, although it can be local instead of global. Furthermore, we propose a procedure to identify failed fitting of the algorithm caused by local convergence to a wrong solution, and thus, it reduces the probability of fitting failure by restarting the algorithm at a different initialization. The proposed robust ellipse fitting method is next extended to the coupled ellipses fitting problem. Both simulated and real data verify the superior performance of the proposed ellipse fitting method over the existing methods.
Chip-based quantum communications

Abstract Quantum communications aim to share encryption keys between the transmitters and receivers governed by the laws of quantum mechanics. Integrated quantum photonics offers significant advantages of dense integration, high stability and scalability, which enables a vital platform for the implementation of quantum information processing and quantum communications. This article reviews recent experimental progress and advances in the development of integrated quantum photonic devices and systems for quantum communications and quantum networks.
Subspace Adversarial Training

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e., the robust accuracy against projected gradient descent (PGD) attack suddenly drops to 0% during the training. In this paper, we approach this problem from a novel perspective of optimization and firstly reveal the close link between the fast-growing gradient of each sample and overfitting, which can also be applied to understand robust overfitting in multi-step AT. To control the growth of the gradient, we propose a new AT method, Subspace Adversarial Training (Sub-AT), which constrains AT in a carefully extracted subspace. It successfully resolves both kinds of overfitting and significantly boosts the robustness. In subspace, we also allow single-step AT with larger steps and larger radius, further improving the robustness performance. As a result, we achieve state-of-the-art single-step AT performance. Without any regularization term, our single-step AT can reach over 51 % robust accuracy against strong PGD-50 attack of radius 8/255 on CIFAR-10, reaching a competitive performance against standard multi-step PGD-10 AT with huge computational advantages. The code is released at https://github.com/nblt/Sub-AT.
Modal analysis of violin bodies with back plates made of different wood species

This research investigated the potential of some European wood species for use in the manufacturing of the back plates of violins as an alternative to the quite rare curly maple wood. An experimental modal analysis was employed for this purpose using the impact hammer method. The modal analysis was performed both on the top and back plates, as individual structures, and then after being integrated into the violin body. The modal analysis envisaged the determination of the eigenfrequencies (natural frequencies), the number of spectral components, and the quality factor, as important indicators of the acoustic performances of a musical instrument. A multi-criteria analysis based on the values obtained for these indicators allowed interesting findings concerning the acoustic properties of the selected wood species (hornbeam, willow, ash, bird-eye maple, walnut, and poplar). Same as curly maple, they all have special aesthetics, but only hornbeam, willow, and ash wood proved to have acoustic potential as well.
An emotion role mining approach based on multiview ensemble learning in social networks

Emotion is a status that combines people’s feelings, thoughts, and behaviors, and plays a crucial role in communication among people. Large studies suggest that human emotions can also be conveyed through online interactions. Previous studies have addressed the mechanism of emotional contagion; however, emotional contagion, through users of online social networks, has not yet been thoroughly researched. Therefore, in this study, initially, the definition of emotion roles, which may play an important role in emotional contagion, is introduced. On this basis, an emotion role mining approach based on multiview ensemble learning (ERM-ME) is proposed to detect emotion roles in social networks by fusing the information contained in different features. The ERM-ME approach includes three stages: detection of emotional communities, local fusion, and global fusion. First, ERM-ME divides emotional communities based on user emotional preferences. Then, emotional features are employed to train basic classifiers, which are then combined into meta-classifiers. Finally, an accuracy-based weighted voting scheme is used to integrate the results of meta-classifiers to achieve a more accurate and comprehensive classification. Experiments and evaluations are performed using Flickr and Microblog datasets to verify the practicability and effectiveness of the proposed method. Extensive experimental results show that the proposed approach outperforms alternative methods. The micro F-score is used as an evaluation indicator. Using the ERM-ME approach, the indicator is improved by approximately 1.09%–14.57% on Flickr and 5.19%–8.95% on Microblog, compared with Graph Convolutional Network, random forest, AdaBoost, bagging, and stacking.
Tracking Objects as Pixel-Wise Distributions

Multi-object tracking (MOT) requires detecting and associating objects through frames. Unlike tracking via detected bounding boxes or center points, we propose tracking objects as pixel-wise distributions. We instantiate this idea on a transformer-based architecture named P3AFormer, with pixel-wise propagation, prediction, and association. P3AFormer propagates pixel-wise features guided by flow information to pass messages between frames. Further, P3AFormer adopts a meta-architecture to produce multi-scale object feature maps. During inference, a pixel-wise association procedure is proposed to recover object connections through frames based on the pixel-wise prediction. P3AFormer yields 81.2% in terms of MOTA on the MOT17 benchmark – highest among all transformer networks to reach 80% MOTA in literature. P3AFormer also outperforms state-of-the-arts on the MOT20 and KITTI benchmarks. The code is at https://github.com/dvlab-research/ ECCV22-P3AFormer-Tracking-Objects-as-Pixel-wise-Distributions.
Learning Predictive Autoscaling Policies for Cloud-Hosted Microservices Using Trace-Driven Modeling

Autoscaling methods are important to ensure response time guarantees for cloud-hosted microservices. Most of the existing state-of-the-art autoscaling methods use rule-based reactive policies with static thresholds defined either on monitored resource consumption metrics such as CPU and memory utilization or application-level metrics such as the response time. However, it is challenging to determine the most appropriate threshold values to minimize resource consumption and performance violations. Whereas, predictive autoscaling methods can help to address these challenges. These methods require considerable time to collect sufficient performance traces representing different resource provisioning possibilities for a target infrastructure to train a useful predictive autoscaling model. In this paper, we tackle this problem by proposing a system that models the response time of microservices through stress testing and then uses a trace-driven simulation to learn a predictive autoscaling model for satisfying response time requirements automatically. The proposed solution reduces the need for collecting performance traces to learn a predictive autoscaling model. Our experimental evaluation on AWS cloud using a microservice under realistic dynamic workloads validates the proposed solution. The validation results show excellent performance to satisfy the response time requirement with only 4.5% extra cost for using the proposed autoscaling method compared to the reactive autoscaling method.