Reviewerly/exHarmony huggingface dataset:
3D path planning and real-time collision resolution of multirotor drone operations in complex urban low-altitude airspace

Drones have been greatly developed to facilitate the progress of various industries. The safe operation of drones in the urban airspace is critical to ensure a reliable and high-efficient urban air traffic system. This work presents a fusion scheme to achieve autonomous drone collision-free path planning considering static obstacles and dynamic threats detected. Firstly, a 3D voxel jump point search (JPS) based path planning model is developed to generate the static collision-free reference path. With the optimization, the reference path is then de-diagonalized, reconstructed, and smoothed to obtain the desired path. Subsequently, a local collision resolution method is proposed to avoid near mid-air collision of the dynamic threats. The method is based on the Markov decision process (MDP) to implement real-time dynamic collision avoidance. Simulations are conducted to verify the performance of the proposed model. The simulation results demonstrate that the proposed model is effective to achieve the autonomous path planning and real-time collision resolution of multirotor drones.
Web remote ideological and political education system constructed by using agent technology

In order to improve the effect of remote ideological and political education, this paper builds a Web ideological and political education system based on Agent technology, and adopts a three-layer abstract system architecture including Web service layer, Agent processing layer and service process layer. Moreover, based on this architecture foundation, this paper proposes an Agent-based Web service integration structure, and illustrates the overall execution process of the system through the execution process of the system integration structure. Then, this paper proposes the organization structure of multi-agent in the Agent processing layer and the organization structure of service process in the service process layer of the system. In addition, this paper uses multi-agent system design to ensure the efficient operation of the entire system, and combines algorithms to implement system resource recommendation modules and practical teaching functions. Finally, this paper designs a control experiment to test the performance of the distance ideological and political education system constructed in this paper. The research results show that the system constructed in this paper has certain practical effects.
Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.
Multi-modal entity alignment in hyperbolic space

Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph (KG), resulting in multi-modal knowledge graphs (MMKG). However, MMKGs usually suffer from low coverage and incompleteness. To mitigate this problem, a viable approach is to integrate complementary knowledge from other MMKGs. To this end, although existing entity alignment approaches could be adopted, they operate in the Euclidean space, and the resulting Euclidean entity representations can lead to large distortion of KG’s hierarchical structure. Besides, the visual information has yet not been well exploited. In response to these issues, in this work, we propose a novel multi-modal entity alignment approach, Hyperbolic multi-modal entity alignment (HMEA), which extends the Euclidean representation to hyperboloid manifold. We first adopt the Hyperbolic Graph Convolutional Networks (HGCNs) to learn structural representations of entities. Regarding the visual information, we generate image embeddings using the densenet model, which are also projected into the hyperbolic space using HGCNs. Finally, we combine the structure and visual representations in the hyperbolic space and use the aggregated embeddings to predict potential alignment results. Extensive experiments and ablation studies demonstrate the effectiveness of our proposed model and its components.
The<b>PySAL</b>Ecosystem: Philosophy and Implementation

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution.
LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks

Due to the purpose of this study that reducing power consumption in the cloud network is based on load balancing, the fitness function measures the load balance between cloud network and servers (the hosts). This technique is appropriate for handling the resource optimization challenges, due to the ability to convert the load balancing problem into an optimization problem (reducing imbalance cost). In this research, combining the results of the particle swarm genetic optimization (PSGO) algorithm and using a combination of advantages of these two algorithms lead to the improvement of the results and introducing a suitable solution for load balancing operation, because in the proposed approach (LBPSGORA), instead of randomly assigning the initial population in the genetic algorithm, the best result is procured by putting the initial population. The LBPSGORA method is compared with PSO, GA, and hybrid GA-PSO. The execution cost, load balancing, and makespan have been evaluated and our method has performed better than similar methods.
Deep transfer learning for conditional shift in regression

Deep transfer learning (DTL) has received increasing attention in smart manufacturing, whereas most current studies focus on the situation of marginal distribution shift in classification. We observe a new regression scenario in machine health monitoring systems (MHMS) with conditional distribution discrepancy across domains and try to propose a general theoretical approach for broader applications. In this paper, we propose a DTL framework CDAR, namely conditional distribution deep adaptation in regression. As only few labeled target data is available, in addition to only considering the prediction accuracy of individual samples, CDAR aims to preserve the global properties of the conditional distribution dominated by the target data. Thus, a hybrid loss function is constructed by combining the mean square error (MSE) and conditional embedding operator discrepancy (CEOD) in CDAR, and the target model is able to be finetuned by minimizing the designed loss function through back-propagation. The performance of the proposed CDAR is compared with two classical marginal distribution adaptation algorithms, TCA and DAN, and a specific method of DTL, FA. Experiments are carried out on two real-world datasets and the results verify the effectiveness of our method.
Exploring evolution of augmented and virtual reality education space in 2020 through systematic literature review

Abstract Research is increasingly being conducted to identify the benefits provided by the latest developments in the AR/VR domain, which has seen an increase in interest as a result of the stay‐at‐home phenomena in 2020. Of particular interest is the application of AR/VR to education, a discipline that has seen a rapid shift to online modules in 2020. To better understand the advancements in AR/VR enabled education, we conducted a systematic literature review consisting of papers published in the year 2020 that focused on AR/VR in the education sector. We particularly focused on papers where studies have evaluated user perceptions in different countries, academic fields, and at varied educational levels. We found that while most papers conducted user studies and evaluated the technical applications of AR/VR, user perceptions, impact, and awareness were not explored in detail. Our findings highlight trends that can drive critically needed innovations through AR/VR especially to help a globalized digital evolution in the education sector.
SALDR: Joint Self-Attention Learning and Dense Refine for Massive MIMO CSI Feedback With Multiple Compression Ratio

The advantages of massive multiple-input multiple-output (MIMO) techniques depend heavily on the accuracy of channel state information (CSI). In frequency division duplexing (FDD) massive MIMO systems, the user equipment (UE) needs to feed downlink CSI back to the base station (BS) through the feedback link. The excessive feedback overheads and low reconstruction accuracy are the main obstacles for actual deployment of FDD massive MIMO systems. In recent years, deep learning (DL) has been widely used to address the above problems. In this letter, we propose a neural network by utilizing the self-attention learning and dense refine (SALDR), which improves the accuracy of CSI feedback. Furthermore, a unified decoder named SALDR-U is designed to realize different compression ratios for CSI feedback without changing any parameter. Simulation results show that the proposed SALDR and SALDR-U outperform the state-of-the-art network in terms of accuracy and overhead of CSI feedback. The source code for all the experiments is available at GitHub.The code of this letter can be downloaded from GitHub link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/XS96/SALDR</uri> .
Popularity Bias in Dynamic Recommendation

Popularity bias is a long-standing challenge in recommender systems: popular items are overly recommended at the expense of less popular items that users may be interested in being under-recommended. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to studying and solving such a bias. However, most existing works situate the popularity bias in a static setting, where the bias is analyzed only for a single round of recommendation with logged data. These works fail to take account of the dynamic nature of real-world recommendation process, leaving several important research questions unanswered: how does the popularity bias evolve in a dynamic scenario? what are the impacts of unique factors in a dynamic recommendation process on the bias? and how to debias in this long-term dynamic process? In this work, we investigate the popularity bias in dynamic recommendation and aim to tackle these research gaps. Concretely, we conduct an empirical study by simulation experiments to analyze popularity bias in the dynamic scenario and propose a dynamic debiasing strategy and a novel False Positive Correction method utilizing false positive signals to debias, which show effective performance in extensive experiments.
Deep learning approach for Translating Arabic Holy Quran into Italian language

Machine translation has become an urgent necessity due to the difference between the languages used within the world's societies. Automatic machine translation uses a computer to translate text or speech from one language to another. Arabic Language is one of the seven united nations officially spoken languages. Few efforts tried to translate officially Arabic language to other languages. The inaccurate Translation of Arabic language might lead to several problem especially when we translate the holy text of Quran into other language. The aim of this paper is to use neural machine translation to enhance the translation quality of Arabic to Italian. The main contribution of this paper is to propose two deep learning models, namely LSTM and GRU, with attention mechanism to translate the Arabic Holy Quran to Italian. The proposed models are evaluated based on the BLEU and ROUGE score. The experimental results indicate that the BLEU and ROUGE score of LSTM have achieved Average score 0.18 and 0.17 on BLEU and ROUGE score respectively. also, the result of GRU have achieved Average score 0.17 and 0.16 on BLEU score and ROUGE score respectively.
Cryptanalysis of hybrid secure image encryption based on Julia set fractals and three-dimensional Lorenz chaotic map

Recently a hybrid secure image encryption scheme based on Julia set and three-dimensional Lorenz chaotic system was suggested. The offered scheme was based on the multiplicative operation carried out by Julia set fractals and bitwise XOR which was implemented by the trajectories obtained from the Lorenz chaotic system. This paper presents the cryptanalysis of the scheme through a chosen-plaintext attack by taking three different chosen images. In this work, a practical chosen-plaintext attack is performed which reveals that the cryptosystem depending on only multiplication and bitwise XOR operation can be effortlessly attacked. The results of the analysis performed on the retrieved image reflect the effectiveness of the chosen-plaintext attack which fruitfully produces the original image.
Adversarial Attacks Against Face Recognition: A Comprehensive Study

Face recognition (FR) systems have demonstrated reliable verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC).In an advanced FR system with deep learning-based architecture, however, promoting the recognition efficiency alone is not sufficient, and the system should also withstand potential kinds of attacks.Recent studies show that (deep) FR systems exhibit an intriguing vulnerability to imperceptible or perceptible but natural-looking adversarial input images that drive the model to incorrect output predictions.In this article, we present a comprehensive survey on adversarial attacks against FR systems and elaborate on the competence of new countermeasures against them.Further, we propose a taxonomy of existing attack and defense methods based on different criteria.We compare attack methods on the orientation, evaluation process, and attributes, and defense approaches on the category.Finally, we discuss the challenges and potential research direction.
Skip-Convolutions for Efficient Video Processing

We propose Skip-Convolutions to leverage the large amount of redundancies in video streams and save computations. Each video is represented as a series of changes across frames and network activations, denoted as residuals. We reformulate standard convolution to be efficiently computed on residual frames: each layer is coupled with a binary gate deciding whether a residual is important to the model prediction, e.g. foreground regions, or it can be safely skipped, e.g. background regions. These gates can either be implemented as an efficient network trained jointly with convolution kernels, or can simply skip the residuals based on their magnitude. Gating functions can also incorporate block-wise sparsity structures, as required for efficient implementation on hardware platforms. By replacing all convolutions with Skip-Convolutions in two state-of-the-art architectures, namely EfficientDet and HRNet, we reduce their computational cost consistently by a factor of 3 ∼ 4× for two different tasks, without any accuracy drop. Extensive comparisons with existing model compression, as well as image and video efficiency methods demonstrate that Skip-Convolutions set a new state-of-the-art by effectively exploiting the temporal redundancies in videos.
Home Action Genome: Cooperative Compositional Action Understanding

Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomicactions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn representations capturing this information. However, there remains a lack of studies that extend action composition and leverage multiple view-points and multiple modalities of data for representation learning. To promote research in this direction, we introduce Home Action Genome (HOMAGE): a multi-view action dataset with multiple modalities and view-points supplemented with hierarchical activity and atomic action labels together with dense scene composition labels. Lever-aging rich multi-modal and multi-view settings, we propose Cooperative Compositional Action Understanding (CCAU), a cooperative learning framework for hierarchical action recognition that is aware of compositional action elements. CCAU shows consistent performance improvements across all modalities. Furthermore, we demonstrate the utility of co-learning compositions in few-shot action recognition by achieving 28.6% mAP with just a single sample.
An efficient adaptive variational quantum solver of the Schrödinger equation based on reduced density matrices

Recently, an adaptive variational algorithm termed Adaptive Derivative-Assembled Pseudo-Trotter ansatz Variational Quantum Eigensolver (ADAPT-VQE) has been proposed by Grimsley et al. (Nat. Commun. 10, 3007) while the number of measurements required to perform this algorithm scales O(N^8). In this work, we present an efficient adaptive variational quantum solver of the Schrodinger equation based on ADAPT-VQE together with the reduced density matrix reconstruction approach, which reduces the number of measurements from O(N^8) to O(N^4). This new algorithm is quite suitable for quantum simulations of chemical systems on near-term noisy intermediate-scale hardware due to low circuit complexity and reduced measurement. Numerical benchmark calculations for small molecules demonstrate that this new algorithm provides an accurate description of the ground-state potential energy curves. In addition, we generalize this new algorithm for excited states with the variational quantum deflation approach and achieve the same accuracy as ground-state simulations.
The effects of target size and error rate on the cognitive demand and stress during augmented reality interactions

This study investigated the effects of target size and error rate on cognitive demand during augmented reality (AR) interactions. In a repeated-measures laboratory study, twenty participants performed two AR tasks (omni-directional pointing and cube placing) with different target sizes and error rates. During the AR tasks, we measured cerebral oxygenation using functional near-infrared spectroscopy (fNIRS), perceived workload using the NASA-TLX questionnaire, stress using the Short Stress State Questionnaire, and task performance (task completion time). The results showed that the AR tasks with more interaction errors increased cerebral oxygenation, perceived workload, and task completion time while the target size significantly affected physical demand and task completion time. These results suggest that appropriate target sizes and low system errors may reduce potential cognitive demand in AR interactions.
Enhanced anomaly scores for isolation forests

• We propose novel anomaly scores for Isolation Forests. • We design weighted variants of the anomaly score that embed additional information. • We propose a novel aggregation function to combine the scores at forest level. • Experiments confirm the suitability of the approach. • The novel proposed scores outperform the original one. Isolation Forest represents a variant of Random Forest largely and successfully employed for outlier detection. The main idea is that outliers are likely to get isolated in a tree after few splits. The anomaly score is therefore a function inversely related to the leaf depth. This paper proposes enhanced anomaly scores of the Isolation Forest by making two different contributions. The first consists in weighing the path traversed by an object to obtain a more informative anomaly score. The second contribution employs a different aggregation function to combine the tree scores. We thoroughly evaluate the proposed methodology by testing it on sixteen datasets.
A Survey of Human‐Centered Evaluations in Human‐Centered Machine Learning

Abstract Visual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. The result is a lack of structure and comparability between evaluations. In this survey, we provide a comprehensive overview of evaluations in the field of human‐centered machine learning. We particularly focus on human‐related factors that influence trust, interpretability, and explainability. We analyze the evaluations presented in papers from top conferences and journals in information visualization and human‐computer interaction to provide a systematic review of their setup and findings. From this survey, we distill design dimensions for structured evaluations, identify evaluation gaps, and derive future research opportunities.
Efficient Distributed Approaches to Core Maintenance on Large Dynamic Graphs

As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage and efficiency. In this article, we investigate distributed approaches to core maintenance on a pregel-like system, which is a famous graph computing system. We first design a core decomposition algorithm to obtain core numbers of vertices in a given graph. Based on it, a distributed batch-stream combined algorithm (DBCA) is devised to efficiently maintain the core numbers when vertex/edge updates happen. In particular, we introduce a new task assignment strategy to DBCA based on diversity of the edge-cores of updated edges. To ensure that DBCA can accurately process core maintenance, we develop a message interaction protocol to resolve the problem of crosstalk among different tasks. Comprehensive experiments have been conducted on real/synthetic graphs, more specifically, in two typical distributed environments built on Supercomputing Center and Alibaba Cloud. The experiment results demonstrate that our proposed algorithms are efficient and scalable.