Reviewerly/exHarmony huggingface dataset:
Multi-UAV Cooperative Task Planning for Border Patrol based on Hierarchical Optimization

In order to improve the patrol efficiency of border patrol drones, based on unmanned aerial vehicle (UAV) border patrol missions in multiple complex environments, this article proposes a whale algorithm based on chaos theory to plan patrol missions for multiple drones. First, according to the terrain the corresponding environmental model is established for the topography and then solved in layers to obtain the number of drones and other information that each base needs to send to the patrol area. Further, the use of drones with cameras and other detection equipment to patrol the scene information and images extract and transfer to the terminal in real time, and further detect suspicious persons and vehicles on the screen. The final simulation results show that the proposed scheme can be effectively applied to the planning of multi-UAV coordinated missions for border patrol.
The Relationship between Fe Mineralization and Magnetic Basement Faults using Multifractal Modeling in the Esfordi and Behabad Areas (BMD), Central Iran

Abstract Multifractal modeling is a mathematical method for the separation of a high potential mineralized background from a non‐mineralized background. The Concentration‐Distance to Fault structures (C‐DF) fractal model and the distribution of the known iron (Fe) deposits/mines seen in the Esfordi and Behabad 1:100,000 sheets from the Bafq region of central Iran are used to distinguish Fe mineralization based on their distance to magnetic basement structures and surface faults, separately, using airborne geophysical data and field surveys. Application of the C‐DF fractal model for the classification of Fe mineralizations in the Esfordi and Behabad areas reveals that the main ones show a correlation with their distance from magnetic basement structures. Accordingly, the distances of Fe mineralizations with grades of Fe higher than 55% (43% &lt; Fe ≤ 60%) are located at a distance of less than 1 km, whereas for surfacial faults with grades of 43% ≤ Fe ≤ 60%, the distances are 3162&lt; DF ≤ 4365 m from the faults. Thus, there is a positive relationship between Fe mineralization and magnetic basement structures. Also, the proximity evidence of Precambrian high‐grade Fe mineralization related to magnetic basement structures indicates syn‐rifting tectonic events. Finally, this C‐DF fractal model can be used for exploration of magmatic and hydrothermal ore deposits.
Preventing insider threats to enhance organizational security: The role of opportunity-reducing techniques

Insider threats are one of the major potential risks jeopardizing organizational security. Many organizations have applied opportunity-reducing techniques to enhance security. These techniques have been classified into hard and soft forms based on their potential propensity to infringe on employees' personal space. Hard form techniques have been widely adopted owing to their efficiency in saving cost and time. However, this study argues that hard form techniques would restrict personal autonomy, leading employees to disrespect the organizational security policies. To test the research hypotheses, a scenario-based questionnaire survey was conducted on 5,158 Prolific Academic members with working experience in the service industry and research and development, and the information industry. Two hundred and fifty-nine valid responses were analyzed using partial least square based structural equation modeling (PLS-SEM). The results show that the hard form techniques induce employees to feel intrusion of their privacy, making them morally irresponsible for their behavior, and even committing an insider attack. Hard form techniques can produce an adverse effect that is opposite to the intended purpose. The results imply that the practical method of making hard controls less intrusive, including the implementation of soft controls and horizontal decision-making processes for security policies, should be explored.
A network-aware and power-efficient virtual machine placement scheme in cloud datacenters based on chemical reaction optimization

In the present article, we propose a virtual machine placement (VMP) algorithm for reducing power consumption in heterogeneous cloud data centers. We propose a novel model for the estimation of power consumption of datacenter’s network. The proposed model is employed to estimate power consumption of a Fat-Tree network. It calculates the traffic of each network layer and uses the results to estimate the average power consumption of each switch in the network, which is used for network power calculation. Further, we employ the chemical reaction optimization (CRO) algorithm as a meta-heuristic algorithm to obtain a power-efficient mapping of virtual machines (VMs) to physical machines (PMs). Moreover, two kinds of solution encoding schemes, namely permutation-based and grouping-based encoding schemes, were utilized for representing individuals in CRO. For each encoding scheme, we designed proper operators required by the CRO for manipulating the molecules in search of more optimal solution candidates. Additionally, we modeled VMs with east–west and north–south communications, and PMs with constrained CPU, memory, and bandwidth capacity. Our network power model is integrated into the CRO algorithms to enable the estimation of both PMs and network power consumption. We compared our proposed methods with a number of similar methods. The evaluation results indicate that the proposed methods perform well and the CRO algorithm with the grouping-based encoding outperforms the rest of the methods in terms of power consumption. The evaluation results also show the significance of network power consumption.
Automatic Modulation Recognition for Overlapping Radar Signals based on Multi-Domain SE-ResNeXt

In the field of electronic reconnaissance, due to the complex electromagnetic environment, the receiver receives the high density signals, and two or even more radar signals are often overlapped. However, the features of the signals are completely interleaved. Especially under low signal-to-noise ratio (SNR), the signals become weak and messy, and the automatic modulation recognition is quite difficult. Through Fourier transform, the overlapping signals can be separated in frequency domain to some extent. The Autocorrelation Function (ACF) has some effect on de-noising, and the time-frequency analysis can reflect the instantaneous frequency of the signal. Therefore, this paper proposes a Multi-Domain Squeeze-and-Excitation ResNeXt (SE-ResNeXt) based on multi-branch feature fusion. In addition to the time domain, the signal is transformed to frequency domain, autocorrelation domain and time-frequency domain, and the high-dimensional feature vectors of signals in different domains are extracted by SE-ResNeXt. Finally, the Squeeze-and-Excitation (SE) block fuses the high-dimensional features of the four branches, the automatic modulation recognition of the overlapping radar signal is realized. The simulation results of six typical radar signals show that the algorithm is robust and effective. At -4dB, the recognition accuracy of the proposed method is more than 92%. Compared with other methods, the proposed method can achieve better performance under low SNR.
Fairness-preserving Group Recommendations With User Weighting

Group recommendations are an extension of "single-user" personalized recommender systems (RS), where the final recommendations should comply with preferences of several group members. An important challenge in group RS is the problem of fairness, i.e., no user's preferences should be largely ignored by the RS. Traditional strategies, such as "least misery" or "average rating", tackle the problem of fairness, but they resolve it separately for each item. This may cause a systematic bias against some group members. In contrast, this paper considers both fairness and relevance as a rank-sensitive list property. We propose EP-FuzzDA algorithm that utilizes an optimization criterion encapsulating both fairness and relevance. In conducted experiments, EP-FuzzDA outperforms several state-of-the-art baselines. Another advantage of EP-FuzzDA is the capability to adjust on non-uniform importance of group members enabling e.g. to maintain the long-term fairness across several recommending sessions.
Stability and Robustness Analysis of Plug-Pulling using an Aerial Manipulator

In this paper, an autonomous aerial manipulation task of pulling a plug out of an electric socket is conducted, where maintaining the stability and robustness is challenging due to sudden disappearance of a large interaction force. The abrupt change in the dynamical model before and after the separation of the plug can cause destabilization or mission failure. To accomplish aerial plug-pulling, we employ the concept of hybrid automata to divide the task into three operative modes, i.e, wire-pulling, stabilizing, and free-flight. Also, a strategy for trajectory generation and a design of disturbance-observer-based controllers for each operative mode are presented. Furthermore, the theory of hybrid automata is used to prove the stability and robustness during the mode transition. We validate the proposed trajectory generation and control method by an actual wire-pulling experiment with a multirotor-based aerial manipulator.
Text Classification Based on Naive Bayes with Adjusted Weights via Frequency Ratio of Feature Words

Text Classification has become a study hot spot because of its wide application in daily life. Many methods are proposed for this task, such as those based on various neural network models. However, the structure of neural network model is complex, so it is very troublesome to modify it. For text classification, there is one of the simplest and most classic methods, which is Naive Bayes. The purpose of this study is to adjust the weight of feature words to improve the effect of Naive Bayes, according to the different frequency ratio of feature words in the target category and other categories. Experimental results show that the performance of the modified Naive Bayes method is generally better than that of the original Naive Bayes method. Specifically, the performance of the two methods is similar without considering smoothing. In the case of considering smoothing, the new method is better than the old one.
A desmoking algorithm for endoscopic images based on improved <scp>U‐Net</scp> model

Abstract In laparoscopic surgery, the smoke generated by operations including electrocautery and laser ablation seriously degrades the quality of endoscopic images. It not only reduces the visibility of the surgery, leading to increased risk of surgery, but also affects the performance of image processing in computer‐assisted surgery such as segmentation, 3D reconstruction and tracking. Therefore, a desmoking algorithm is required to eliminate smoke in endoscopic images. In this article, we study a U‐Net model that can eliminate smoke of the laparoscopic image in real‐time and preserve the natural appearance of the organ surface. Our method is based on the improved U‐Net in which convolutional block attention module is used as an embedded guide mask of the decoder part. The laparoscopic image dataset is provided by the Hamlyn Center, and the Blender software is used to simulate various situations of smoke added to the laparoscopic image for training and testing. For the proposed method, the peak signal‐to‐noise ratio value is up to 29.27 and the structural similarity index is up to 0.945 over the test images. The experimental results demonstrate that the proposed method achieves a better performance than other six existing methods, which is applicable for real‐time endoscopic image processing.
“It’s Just a Graph” – The Effect of Post-Hoc Rationalisation on InfoVis Evaluation

A growing body of work in InfoVis explores its user experience (UX), however, emotions remain underexplored. Our study reveals barriers for investigating personal connection and emotional reaction to visualisations. This provides an explanation for why the role of emotions so far received little attention in InfoVis. Twenty-four participants viewed two traditional data visualisations, answered UX questionnaires for each, and were interviewed about their experience. Our findings show that traditional visualisations are seen as ‘just a graph’, that represents neutral information. Participants referred to aesthetics, legibility, and usability, instead of the actual topic. Moreover, to make sense of the data, emotions have to be separated from it. We found four possible explanations underlying this belief and argue that a form of post-hoc rationalisation takes place, which obscures people’s initial connections and affective responses to visualisations. Based on these findings, we discuss implications for future research on the UX of visualisations.
Algebraic-geometric characterization of tripartite entanglement

To characterize entanglement of tripartite $\mathbb{C}^d\otimes\mathbb{C}^d\otimes\mathbb{C}^d$ systems, we employ algebraic-geometric tools that are invariants under Stochastic Local Operation and Classical Communication (SLOCC), namely $k$-secant varieties and one-multilinear ranks. Indeed, by means of them, we present a classification of tripartite pure states in terms of a finite number of families and subfamilies. At the core of it stands out a fine-structure grouping of three-qutrit entanglement.
A Survey on Sentiment Analysis in Persian: a Comprehensive System Perspective Covering Challenges and Advances in Resources and Methods

Social media has been remarkably grown during the past few years. Nowadays, posting messages on social media websites has become one of the most popular Internet activities. The vast amount of user-generated content has made social media the most extensive data source of public opinion. Sentiment analysis is one of the techniques used to analyze user-generated data. The Persian language has specific features and thereby requires unique methods and models to be adopted for sentiment analysis, which are different from those in English language. Sentiment analysis in each language has specified prerequisites; hence, the direct use of methods, tools, and resources developed for English language in Persian has its limitations. The main target of this paper is to provide a comprehensive literature survey for state-of-the-art advances in Persian sentiment analysis. In this regard, the present study aims to investigate and compare the previous sentiment analysis studies on Persian texts and describe contributions presented in articles published in the last decade. First, the levels, approaches, and tasks for sentiment analysis are described. Then, a detailed survey of the sentiment analysis methods used for Persian texts is presented, and previous relevant works on Persian Language are discussed. Moreover, we present in this survey the authentic and published standard sentiment analysis resources and advances that have been done for Persian sentiment analysis. Finally, according to the state-of-the-art development of English sentiment analysis, some issues and challenges not being addressed in Persian texts are listed, and some guidelines and trends are provided for future research on Persian texts. The paper provides information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete sentiment analysis system.
Multimodal Learning for Temporally Coherent Talking Face Generation With Articulator Synergy

Talking face generation is a demanding task to synthesize a high quality video with accurate lip synchronization and rhythmic head motion. Any subtle artifacts could be sensitively captured by humans and lead to poor visual quality. Existing methods tend to employ a conditional generation solution, which introduces facial landmarks to bridge the input information and output videos. However, these methods always suffer from unrealistic facial animations, because 1) they only take single-mode input, but ignore the complementarity of multimodal inputs for lip-sync improvement; 2) they only explore lip movements, but ignore the articulator synergy between lips and jaw; 3) they generate each video frame in a temporal-independent way, but ignore the temporal continuity among the entire video. To address these limitations, in this paper, we present a novel method to generate realistic and temporally coherent talking heads by considering multimodal inputs, articulator synergy, inter-frame consistency and intra-frame consistency. Firstly, for landmark prediction, a novel Multiple Synergy Network (MSN) is proposed to improve the accuracy of landmark prediction by incorporating multimodal inputs (i.e., audio and text inputs). Besides, instead of merely considering lip landmarks, we also explore the jaw movements to ensure articulator synergy among lips and jaw. Secondly, for realistic video generation, a Video Consistency Network (VCN) is proposed conditioned on the predicted landmarks. In VCN, the optical flow is adopted to model the temporal continuity between frames to ensure inter-frame consistency. Meanwhile, a mouth generation branch is proposed to enhance mouth texture and the corresponding mouth mask is employed to ensure intra-frame consistency between the mouth area and the others. Extensive experiments demonstrate that our approach exhibits excellent superiority on lip-sync and can generate photo-realistic facial animations. Project is available at http://imcc.ustc.edu.cn/project/tfgen/.
3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting

The instability and variability of solar irradiance induces great challenges for the management of photovoltaic water pumping systems. Accurate global horizontal irradiance (GHI) forecasting is a promising technique to solve this problem. To improve short-term GHI forecasting accuracy, ground-based sky image is valuable due to its correlation with solar generation. In previous studies, great efforts have been made to extract numerical features from sky image for data-driven solar irradiance forecasting methods, e.g., based on pixel-value color information, and based on the cloud motion detection method. In this work, we propose a novel feature extracting method for GHI forecasting that a three-dimensional (3D) convolutional neural network (CNN) is developed to extract features from sky images with efficient training strategies. Popular machine learning algorithms are introduced as GHI forecasting models and corresponding forecasting accuracy is fully explored with different input features on a large dataset. The numerical experiment illustrates that the minimum average root mean square error (RMSE) of 62 W/m2 is achieved by the proposed method with 15.2% improvement in Skill score against baseline forecasting method.
Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning

Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.Specifically, we propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into the training set of a system trained using back-translation.This sample is designed to induce a specific, targeted translation behaviour, such as peddling misinformation.We present two methods for crafting poisoned examples, and show that only a tiny handful of instances, amounting to only 0.02% of the training set, is sufficient to enact a successful attack.We outline a defence method against said attacks, which partly ameliorates the problem.However, we stress that this is a blind-spot in modern NMT, demanding immediate attention.
Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network

<h2>Summary</h2> In this article, we propose a new approach to analyze large genomics data. We considered individual genetic variants as pixels in an image and transformed a collection of variants into an artificial image object (AIO), which could be classified as a regular image by CNN algorithms. Using schizophrenia as a case study, we demonstrate the principles and their applications with 3 datasets. With 4,096 SNVs, the CNN models achieved an accuracy of 0.678 ± 0.007 and an AUC of 0.738 ± 0.008 for the diagnosis phenotype. With 44,100 SNVs, the models achieved class-specific accuracies of 0.806 ± 0.032 and 0.820 ± 0.049, and AUCs of 0.930 ± 0.017 and 0.867 ± 0.040 for the bottom and top classes stratified by the patient's polygenic risk scores. These results suggest that, once transformed to images, large genomics data can be analyzed effectively with image classification algorithms.
Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.
A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning

We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary combinatorial problems. We test the proposed algorithm on an ensemble learning problem. Ensembling several models is a common Machine Learning technique to achieve better performance. We consider ensembles of several models trained on disjoint subsets of a dataset. Finding the best dataset partitioning is naturally a combinatorial non-binary optimization problem. Fitness function evaluations can be extremely expensive if complex models, such as Deep Neural Networks, are used as learners in an ensemble. Therefore, the number of fitness function evaluations is typically limited, necessitating expensive optimization techniques. In our experiments we use five classification datasets from the OpenML-CC18 benchmark and Support-vector Machines as learners in an ensemble. The proposed algorithm demonstrates better performance than alternative approaches, including Bayesian optimization algorithms. It manages to find better solutions using just several thousand fitness function evaluations for an ensemble learning problem with up to 500 variables.