Reviewerly/exHarmony huggingface dataset:
Constrained Differential Dynamic Programming Revisited

Differential Dynamic Programming (DDP) has become a well established method for unconstrained trajectory optimization. Despite its several applications in robotics and controls, however, a widely successful constrained version of the algorithm has yet to be developed. This paper builds upon penalty methods and active-set approaches towards designing a Dynamic Programming-based methodology for constrained optimal control. Regarding the former, our derivation employs a constrained version of Bellman&#x2019;s principle of optimality, by introducing a set of auxiliary slack variables in the backward pass. In parallel, we show how Augmented Lagrangian methods can be naturally incorporated within DDP, by utilizing a particular set of penalty-Lagrangian functions that preserve second-order differentiability. We demonstrate experimentally that our extensions (individually and combinations thereof) enhance significantly the convergence properties of the algorithm, and outperform previous approaches on a large number of simulated scenarios.
A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data

Flow cytometry (FC) is critical for the diagnosis and monitoring of hematologic malignancies. Machine learning (ML) methods rapidly classify multidimensional data and should dramatically improve the efficiency of FC data analysis. We aimed to build a model to classify acute leukemias, including acute promyelocytic leukemia (APL), and distinguish them from nonneoplastic cytopenias. We also sought to illustrate a method to identify key FC parameters that contribute to the model's performance.Using data from 531 patients who underwent evaluation for cytopenias and/or acute leukemia, we developed an ML model to rapidly distinguish among APL, acute myeloid leukemia/not APL, acute lymphoblastic leukemia, and nonneoplastic cytopenias. Unsupervised learning using gaussian mixture model and Fisher kernel methods were applied to FC listmode data, followed by supervised support vector machine classification.High accuracy (ACC, 94.2%; area under the curve [AUC], 99.5%) was achieved based on the 37-parameter FC panel. Using only 3 parameters, however, yielded similar performance (ACC, 91.7%; AUC, 98.3%) and highlighted the significant contribution of light scatter properties.Our findings underscore the potential for ML to automatically identify and prioritize FC specimens that have critical results, including APL and other acute leukemias.
Institutional factors, religiosity, and entrepreneurial activity: A quantitative examination across 85 countries

Given the ongoing debate on the relationship between institutions and entrepreneurship, this study aims to better understand the joint effects of two country-level formal institutions, government effectiveness and political freedom, and whether the informal institution of religiosity moderates this relationship. To achieve this goal, the current study used multiple datasets, including the Global Entrepreneurship Monitor (GEM), World Governance Indicators (WGIs), and the WIN-Gallup International Global Index of Religiosity and Atheism. The data cover 85 countries from 2010 to 2017. The results suggest that the rate of entrepreneurial activity across nations is shaped by the interplay between formal and informal institutions.
Spatial Reliability Enhanced Correlation Filter: An Efficient Approach for Real-Time UAV Tracking

Traditional discriminative correlation filter (DCF) has received great popularity due to its high computational efficiency. However, the lightweight framework of DCF cannot promise robust performance when the tracker faces appearance variations within the background. These unpredictable appearance variations always distract the filter. Most existing DCF-based trackers either utilize deep convolutional features or incorporate additional constraints to elevate tracking robustness. Despite some improvements, both of them hamper the tracking speed and can only roughly alleviate the distractions of appearance variations. In this paper, a novel spatial reliability enhanced learning strategy is proposed to handle the problems aforementioned. By monitoring the variation of response produced in detection phase, a dynamic reliability map is generated to indicate the reliability of each background subregion. Then, label adjustment is conducted to repress the distractions of these unreliable areas. Compared with the conventional way of constraint where a new term is always added to realize the desired goal, label adjustment is simultaneously more efficient and effective. Moreover, to promise the accuracy and dependability of the reliability map, an adaptively updated response pool recording reliable historical response values is proposed. Extensive and exhaustive experiments on three challenging unmanned aerial vehicle (UAV) benchmarks, i.e., UAV123@10fps, DTB70 and UAVDT, which totally include 243 video sequences, validate the superiority of the proposed method against other state-of-the-art trackers and exhibit a remarkable generality in a variety of scenarios. Meanwhile, the tracking speed of 65.2FPS on a cheap CPU makes it suitable for real-time UAV applications.
Sequence Labeling with Meta-Learning

Recent neural architectures in sequence labeling have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for sequence labeling under homogeneous and heterogeneous settings. We propose MetaSeq, a novel meta-learning approach for domain adaptation in sequence labeling. Specifically, MetaSeq incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaSeq is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaSeq on named entity recognition, part-of-speech tagging and slot filling tasks under homogeneous and heterogeneous settings. The experimental results show that MetaSeq achieves state-of-the-art performance against eight baselines. Impressively, MetaSeq surpasses the in-domain performance using only 16.17% and 7% of target domain data on average for homogeneous settings, and 34.76%, 24%, 22.5% of target domain data on average for heterogeneous settings.
Co-Design with Older Adults: Examining and Reflecting on Collaboration with Aging Communities

Co-design methods have involved older adults in the design process to fill the knowledge gap that younger adult designers might encounter when designing for an aging population. A focus of co-design means establishing equal and equitable relationships between users and designers. To understand the factors that contribute to equal collaborations between older adults and student designers, we conducted 12 co-design sessions with 16 older adults and 11 student designers. We examined their interactions by adapting a framework initially aimed to understand the child-adult design partnership. We also analyzed student designers' reflections to understand their experiences and learnings from designing with older adults. Our findings demonstrate that developing a design partnership is complex. The framework helped surface factors like sharing life experiences and role ownership that influenced balanced or unbalanced interactions. Through the student designers' reflections, we found that student designers identified challenges they encountered and the assumptions they had about the older adult population. We believe that immersing students in a co-design experience with older adults and leveraging reflection activities provides an educational and meaningful experience to the design students.
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception

Multi-object tracking and segmentation (MOTS) is a critical task for autonomous driving applications. The existing MOTS studies face two critical challenges: 1) the published datasets inadequately capture the real-world complexity for network training to address various driving settings; 2) the working pipeline annotation tool is under-studied in the literature to improve the quality of MOTS learning examples. In this work, we introduce the DG-Labeler and DGL-MOTS dataset to facilitate the training data annotation for the MOTS task and accordingly improve network training accuracy and efficiency. DG-Labeler uses the novel Depth-Granularity Module to depict the instance spatial relations and produce fine-grained instance masks. Annotated by DG-Labeler, our DGL-MOTS dataset exceeds the prior effort (i.e., KITTI MOTS and BDD100K) in data diversity, annotation quality, and temporal representations. Results on extensive cross-dataset evaluations indicate significant performance improvements for several state-of-the-art methods trained on our DGL-MOTS dataset. We believe our DGL-MOTS Dataset and DG-Labeler hold the valuable potential to boost the visual perception of future transportation. Our dataset and code are available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
An Access Control Scheme Using Heterogeneous Signcryption for IoT Environments

When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature, however, the majority of these schemes are based on homogeneous nature. This will be neither adequate nor practical for heterogeneous IoT environments. In addition, these schemes are based on bilinear pairing and elliptic curve cryptography, which further requires additional processing time and more communication overheads that is inappropriate for real-time communication. Consequently, this paper aims to solve the above-discussed issues, we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve. Besides the security services such as replay attack prevention, confidentiality, integrity, unforgeability, non-repudiations, and forward secrecy, the proposed scheme has very low computational and communication costs, when it is compared to existing schemes. This is primarily because of hyperelliptic curve lighter nature of key and other parameters. The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends (Constraint Logic-based Attack Searcher (CL-b-AtSER) and On-the-Fly Model Checker (ON-t-FL-MCR)) proved to be SAFE when the presented scheme is coded in HLPSL language. This scheme was proven to be capable of preventing a variety of attacks, including confidentiality, integrity, unforgeability, non-repudiation, forward secrecy, and replay attacks.
Achieving Privacy-Preserving Online Diagnosis With Outsourced SVM in Internet of Medical Things Environment

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this article, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.
Perceiving Temporal Environment for Correlation Filters in Real-Time UAV Tracking

Discriminative correlation filter (DCF)-based methods applied for UAV object tracking have received widespread attention due to their high efficiency. However, these methods are usually troubled by the boundary effect. Besides, the violent environment variations severely confuse trackers that neglect temporal environmental changes among consecutive frames, leading to unwanted tracking drift. In this letter, we propose a novel DCF-based tracking method to promote the insensitivity of the tracker under uncertain environmental changes. Specifically, a regularization term is proposed to learn the environment residual between two adjacent frames, which can enhance the discrimination and insensitivity of the filter in fickle tracking scenarios. Further, we design an efficient strategy to acquire the environment information based on the current observation without additional computation. Exhausted experiments are conducted on two well-known UAV benchmarks, <i>i.e.</i>, UAV123_10fps and DTB70. Results verify that the proposed tracker has comparable performance with other 22 state-of-the-art trackers while running at <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 53 FPS on a low-cost CPU.
Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition

First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that are yet to be addressed in this context. Indeed, the information extracted from learned representations suffers from an intrinsic "environmental bias". This strongly affects the ability to generalize to unseen scenarios, limiting the application of current methods to real settings where labeled data are not available during training. In this work, we introduce the first domain generalization approach for egocentric activity recognition, by proposing a new audiovisual loss, called Relative Norm Alignment loss. It rebalances the contributions from the two modalities during training, over different domains, by aligning their feature norm representations. Our approach leads to strong results in domain generalization on both EPIC-Kitchens-55 and EPIC-Kitchens-100, as demonstrated by extensive experiments, and can be extended to work also on domain adaptation settings with competitive results.
MAppGraph: Mobile-App Classification on Encrypted Network Traffic using Deep Graph Convolution Neural Networks

Identifying mobile apps based on network traffic has multiple benefits for security and network management. However, it is a challenging task due to multiple reasons. First, network traffic is encrypted using an end-to-end encryption mechanism to protect data privacy. Second, user behavior changes dynamically when using different functionalities of mobile apps. Third, it is hard to differentiate traffic behavior due to common shared libraries and content delivery within modern mobile apps. Existing techniques managed to address the encryption issue but not the others, thus achieving low detection/classification accuracy. In this paper, we present MAppGraph, a novel technique to classify mobile apps, addressing all the above issues. Given a chunk of traffic generated by an app, MAppGraph constructs a communication graph whose nodes are defined by tuples of IP address and port of the services connected by the app, edges are established by the weighted communication correlation among the nodes. We extract information from packet headers without analyzing encrypted payload to form feature vectors of the nodes. We leverage deep graph convolution neural networks to learn the diverse communication behavior of mobile apps from a large number of graphs and achieve a fast classification. To validate our technique, we collect traffic of a hundred mobile apps on the Android platform and run extensive experiments with various experimental scenarios. The results show that MAppGraph significantly improves classification accuracy by up to 20% compared to recently developed techniques and demonstrates its practicality for security and network management of mobile services.
Classification and identification of different minerals in the Mediterranean sediments using PSA, FTIR, and XRD techniques

While the Mediterranean coastal region of the Nile Delta is critical socioeconomically, it is under significant environmental stress due to the growing load from diverse land-based activities. This study examines the fluctuations in the concentrations of several minerals in sediments from depths ranging from 10 to 50 m. This study is considering the first work that carries the identification of different minerals by using two different techniques: Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction technique (XRD), in this area. At the same time, the particle size was also assessed. The particle size analyzer (PSA) results showed that the grain size analyses in the sediments varied between slightly gravelly sand, slightly gravelly mud, and silty sand. Mineral identification using FTIR and XRD analysis proved that some minerals such as aragonite, calcite, quartz, kaolinite, montmorillonite, albite, muscovite, and microcline have been presented in most of the studied locations.
Implicit Feedbacks are Not Always Favorable: Iterative Relabeled One-Class Collaborative Filtering against Noisy Interactions

Due to privacy concerns, there is a rising favor in Recommender System community for the One-class Collaborative Filtering (OCCF) framework, which predicts user preferences only based on binary implicit feedback (e.g., click or not-click, rated or unrated). The major challenge in OCCF problem stems from the inherent noise in implicit interaction. Previous approaches have taken into account the noise in unobserved interactions (i.e., not-click only means a missing value, rather than negative feedback). However, they generally ignore the noise in observed interactions (i.e., click does not necessarily represent positive feedback), which might induce performance degradation. To attack this issue, we propose a novel iteratively relabeling framework to jointly mitigate the noise in both observed and unobserved interactions. As the core of the framework, the iterative relabeling module exploits the self-training principle to dynamically generate pseudo labels for user preferences. The downstream module for a recommendation task is then trained with the refreshed labels where the noisy patterns are largely alleviated. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed methods.
Contrastive Feature Loss for Image Prediction

Training supervised image synthesis models requires a critic to compare two images: the ground truth to the result. Yet, this basic functionality remains an open problem. A popular line of approaches uses the L1 (mean absolute error) loss, either in the pixel or the feature space of pretrained deep networks. However, we observe that these losses tend to produce overly blurry and grey images, and other techniques such as GANs need to be employed to fight these artifacts. In this work, we introduce an information theory based approach to measuring similarity between two images. We argue that a good reconstruction should have high mutual information with the ground truth. This view enables learning a lightweight critic to "calibrate" a feature space in a contrastive manner, such that reconstructions of corresponding spatial patches are brought together, while other patches are repulsed. We show that our formulation immediately boosts the perceptual realism of output images when used as a drop-in replacement for the L1 loss, with or without an additional GAN loss.
A comparative study on the surface radiation characteristics of photovoltaic power plant in the Gobi desert

In order to identify impacts of photovoltaic (PV) power plant on surface radiation, this paper conducted a comparative study on the surface radiation and surface albedo characteristics between the PV site and reference site in the Gobi area in Xinjiang, China in the summer of 2020. The results show that, the upward long-wave radiation at the PV site was significantly lower than that at the reference site in the night, while the two were basically the same during the daytime. The average surface albedo at the PV site (0.14) was 39.1% lower than that at the reference site (0.23). Net radiation was higher at the PV site than that at the reference site throughout the whole day. The average net radiation of the underlying surface at the PV site was 30.7% more than that at the reference site, 75% of which was contributed by lower upward shortwave radiation and 25% was due to nocturnal lower upward longwave radiation at the PV site.
Towards Understanding and Supporting Journalistic Practices Using Semi-Automated News Discovery Tools

Journalists are routinely challenged with monitoring vast information environments in order to identify what is newsworthy and of interest to report to a wider audience. In a process referred to as computational news discovery, alerts and leads based on data-driven algorithmic analysis can orient journalists' attention to events, documents, or anomalous patterns in data that are more likely to be newsworthy. In this paper we prototype one such news discovery tool, Algorithm Tips, which we designed to help journalists find newsworthy leads about algorithmic decision-making systems used across all levels of U.S. government. The tool incorporates algorithmic, crowdsourced, and expert evaluations into an integrated interface designed to support users in making editorial decisions about which news leads to pursue. We then present an evaluation of our prototype based on an extended deployment with eight professional journalists. Our findings offer insights into journalistic practices that are enabled and transformed by such news discovery tools, and suggest opportunities for improving computational news discovery tool designs to better support those practices.