Miracle-dz/UniFAR-FTUs huggingface dataset:
Certifiably Optimal and Robust Camera Pose Estimation From Points and Lines

The Perspective-n-Point-and-Line (PnPL) problem, as a cornerstone in geometric computer vision, seeks to estimate the absolute pose of a calibrated camera from 3D-to-2D point and line correspondences. In this paper, we present a certifiably globally optimal and robust solution to the PnPL problem with a large number of outliers among the correspondences. Our first contribution is to reformulate the general PnPL problem as a novel constrained global optimization problem of Sum-of-Squares (SOS) polynomials using generalized geometric distances for both points and lines. Our second contribution is to efficiently solve this non-convex optimization problem by reducing it to an equivalent convex Linear Matrix Inequality (LMI) problem via a series of SOS relaxations. With these two contributions, we can develop a non-minimal solver, named SPnPL, for the outlier-free cases. The third contribution is to further add a Graduated Non-Convexity (GNC) cost function to SPnPL so as to remove outliers through closed-form iterations, which leads to a robust solver, named GNC-SPnPL. Both synthetic and real-data experiments confirm that SPnPL can mostly outperform the existing state-of-the-art PnPL algorithms and GNC-SPnPL can render accurate results even when 85% of the correspondences are outliers.
Minimum Time—Minimum Jerk Optimal Traffic Management for AGVs

A combined minimum time - minimum jerk traffic management system for the vehicle coordination in an automated warehouse is presented. The algorithm is organised in two steps: in the first, a simple minimum time optimisation problem is solved, in the second step, this time-optimal solution is refined into a smooth minimum jerk plan for the autonomous forklifts in order to avoid impulsive forces that may unbalance the vehicle. For the first step, we propose a novel approach based on Linear Programming, which guarantees convergence to the optimal solution starting from a feasible point, and a low computational overhead, which makes it suitable for real-time applications. The output of this step is a piecewise constant velocity profile for all the moving robots that ensures collision avoidance. The second step takes such speed profile and generates its smoothed version, which minimises the jerk while respecting the same levels of safety of the solution generated by the first step. We discuss the different solutions with simulation and experimental data.
A Memetic Algorithm for Community Detection in Signed Networks

Community discovery (i.e. community detection) in signed networks is a division of nodes, such that the edges in the communities are positive and the edges between the communities are negative. Davis and Harary have solved the problem of community detection when a signed graph is balanced or weakly balanced. When the signed network is unbalanced, community detection becomes very complex. In this paper, we propose a novel memetic algorithm (MA) called MACD-SN for community partition (i.e. community detection) in signed networks. Firstly, we present a novel initialization algorithm used in initialization of MACD-SN. This method can accelerate the convergence rate of MACD-SN algorithm. Next, in addition to using frequently-used variation operation (in this paper, variation and mutation are interchangeable), this paper presents a novel crossover operation and a novel variation operation, which contributes to increasing the correctness of the MACD-SN algorithm’s operation result and reduces its running time. Lastly, this paper proposes a new local search algorithm, which may enable the algorithm’s result to jump away the local best result with a certain probability and draw near the global best result quickly. For testing the performance of MACD-SN algorithm, we have done many experiments using five kinds of synthetic signed networks and five real-world signed networks. The test outcomes show that the proposed algorithm is valid and efficient for signed network cluster partition (i.e. community detection).
Traffic Density Classification Using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads

Recently, with the rapid growth of Deep Learning models for solving complicated classification problems, urban sound classification techniques have been attracted more attention. In this paper, we take an investigation on how to apply this approach for the transportation domain. Specifically, traffic density classification based on the road sound datasets, which have been recorded and preprocessed on the urban road network, is taken into account. In particular, state-of-the-art methods for analyzing and extracting sound datasets have taken into account for the classification problem of traffic flow. Consequently, this study focuses on three main processes which are: i) generating image representation for the sequences of the road sound datasets; ii) proposing a convolutional neural network model for the feature extraction; iii) adopting a hybrid approach for the classification stage by combining convolutional neural network with other machine learning models. Regarding the experiment, the road sound dataset has been collected at an urban asymmetric road with different time periods (e.g., morning and evening) in order to evaluate our proposed method. Specifically, the implementations show promising results in which the accuracies are able to achieve from 92% to 95% for classifying traffic densities with different time periods.
Predictive control for networked systems affected by correlated packet loss

In this paper, we consider the predictive control problem of designing receding horizon controllers for networked linear systems subject to random packet loss in the controller to actuator link. The packet dropouts are temporarily correlated in the sense that they obey a Markovian transition model. Our design task is to solve the optimal controller that minimizes a given receding horizon cost function, using the available packet loss history. Due to the correlated nature of the packet loss, standard linear quadratic regulator methods do not apply. We first present the optimal control law by considering the correlations. This controller turns out to depend on the packet loss history and would typically require a large lookup table for implementation when the Markovian order is high. To address this issue, we present and compare several suboptimal design approaches to reduce the number of control laws.
Ocorrência e ecologia trófica de quatro espécies de Astyanax (Characidae) em diferentes rios da bacia do rio Tibagi, Paraná, Brasil

Stretches of the Rivers Fortaleza, Iapo and Tibagi were monthly sampled with gillnets of different mesh sizes and castnets from May 2001 to April 2002 in the Tibagi River basin. Specimens of four species of the Astyanax genera were captured. In order to assess how they use habitat and food resources in the different rivers, their constancy was calculated and the number of specimens was compared by using the Bray-Curtis similarity analysis. The composition (percentage) of food items consumed was ordered by using the Principal Components Analysis (PCA). The proportion of different food items in the diet was assessed and classified according to type (animal or plant material) and origin (allochtonous or autochtonous). The low similarity and constancy showed differences in the type of habitat preferred by the four fish species. Astyanax altiparanae Garutti & Britski, 2000 and A. fasciatus (Cuvier, 1819) were present in the large rivers but were constant only in the Tibagi River. Astyanax eigenmanniorum (Cope, 1894) was present in all rivers but prefers habitats in the mouth of affluents of large rivers. Astyanax scabripinnis (Jenyns, 1842) was restricted to Fortaleza River, a small Tibagi River affluent. The number of food items consumed by the different species varied from 11 to 23. Among the main food, three items were common for the four species: 1- plant residuals (leaves and seeds from the earth community of plants), 2- insect residuals (from different origins and unidentified) and 3- aquatic vegetation. So plants and insects were the main food consumed by all the species. Astyanax eigenmanniorum showed the highest distinction in the diet composition among the species studied. Such distinction was due to its ability of exploring an abundant food resource: the aquatic plants that were consumed together with portions of coal/sediments near the rocky shores at the Iapo River. The similarity of the origin and type of food consumed by the four species and the differences in terms of the habitat occupied may explain why one species is abundant and constant in only one of the river stretches studied.
Diversity and composition of dead wood inhabiting bryophyte communities in European beech forests

Species composition and diversity of bryophyte communities occurring on dead beech trees were analyzed in five European countries (Slovenia, Hungary, The Netherlands, Belgium and Denmark). Altogether 1.009 trees were inventoried in 19 beech dominated forest reserves. The differences in the composition of bryophyte communities were high among countries and it overwhelms the effect of decay stages, which affect the species com-position at local scale. Well decayed trees were dominated by epixylic species in Slovenia, while in other countries the proportion of opportunistic bryophytes was much higher and in Atlantic countries uproot species are also common. In Slovenia hepatics, in Hungary and Denmark pleurocarps, and in The Netherlands and Belgium acrocarps were the most fre-quent. Diversity of communities differed considerably among regions. Slovenian sites were the hotspot of bryophyte diversity characterized by high species richness (both tree and country level) and a high fraction of rare and threatened species. This richness is most likely caused by the combination of high air humidity combined with a very high degree of natu-ralness of the Slovenian sites. The diversity of Hungarian stands was intermediate reflecting the relatively high naturalness of the study sites, as well as a rather continental climate sub-optimal for epixylic liverworts. Atlantic forests have deteriorated bryophyte communities on dead trees, dominated by a few opportunistic species. This is partly a result of habitat frag-mentation and past and recent management of forest. Most of the stands the amount and quality of dead wood is not suitable for species rich epixylic bryophyte communities.
An Automatic Advisor for Refactoring Software Clones Based on Machine Learning

To assist developers refactored code and to enable improvements to software quality when numbers of clones are found in software programs, we require an approach to advise developers on what a clone needs to refactor and what type of refactoring is needed. This paper suggests a unique learning method that automatically extracts features from the detected code clones and trains models to advise developers on what type needs to be refactored. Our approach differs from others, which specifies types of refactored clones as classes and creates a model for detecting the types of refactored clones and the clones which are anonymous. We introduce a new method by which to convert refactoring clone type outliers into $Unknown~clone~set$ to improve classification results. We present an extensive comparative study and an evaluation of the efficacy of our suggested idea by using state-of-the-art classification models
Deep Recurrent Q-Learning for Research on Complex Economic System

Complex economic system theory studies the socio-economic system in a dynamic, complex ideas. It is a more comprehensive economic theory established in non-equilibrium basis. At present, under the guidance of complex economic system theory, multi-agent based modeling and simulation is a universal research method to study economic system. However, the agent's behavior usually is fixed according to the simulation software's setting, which is difficult to explain the phenomena of feedback and emergence in the economic system. To address this problem, we first design a simulation economic system. In the system, there are two types of abstract micro economic agents, bank and enterprise. An improved version of Deep Q-Network (DQN), Deep Recurrent Q-Network (DRQN) was introduced to handle the decision-making of micro economic agents. Finally, we studied the observed behavior of agents in the experiment. The experimental results show that the behavior of economic agents can actively adapt to changes in the system environment and continue to be optimized with the accumulation of experience. Because of taking non-fixed decision-making strategies, this method can reflect the phenomena of internal feedback and emergence in economic system. Therefore, simulation based on DRQN is a simulation method that can better reflect agent decision-making in real economic system and it can play an important role in the study of complex economic system.
Defeating Protocol Abuse with P4: Application to Explicit Congestion Notification

In recent years, programmable data planes enabled by the protocol independent switch architecture (PISA) allowed the relocation of network functions closer to traffic flows and thereby the ability to react in real-time to network events. However, expressing complex and stateful network monitoring functions using state-of-the-art data plane programming languages such as P4 still remain challenging. In this context, we propose a method for modeling a stateful security monitoring function as an Extended Finite State Machine (EFSM) and express the EFSM using P4 language abstractions. We demonstrate the feasibility and benefit of our proposed approach in detecting and mitigating Explicit Congestion Notification (ECN) protocol abuse without any TCP protocol modification. Our evaluation shows that the proposed security monitoring function can restore 24.67% throughput loss caused by misbehaving TCP end-hosts while ensuring fair share of bandwidth among TCP flows.
SAMPLING POPULATIONS OF PINE LEAF CHERMID PINEUS PINIFOLIAE (HOMOPTERA: CHERMIDAE): II. ADULT GALLICOLAE ON THE SECONDARY HOST

Abstract Procedures for absolute population sampling of the gallicola migrans stage of the pine leaf chermid on its secondary host are described. This involves determining the mean numbers of gallicolae per fascicle on one twig from each of 29–240 pines per stand and estimating the numbers of fascicles in the stand from the distribution of stem diameters. Patterns and distributions of the gallicolae within trees and stands are discussed with comments on their biological significance.
Research Progress on Generative Adversarial Network with its Applications

As a new unsupervised learning algorithm framework, generative adversarial networks(GAN) have been favored by more and more researchers, and it has become a research hotspot now. GAN is inspired by the two-person zero-sum game theory in game theory. Its unique adversarial training idea can generate high-quality samples and has more powerful feature learning and feature expression capabilities than traditional machine learning algorithms. At present, GAN has achieved remarkable success in the field of computer vision, especially in the field of sample generation. Every year, a large number of GAN-related research papers are produced, reflecting the fiery degree of research on GAN model. Aiming at the hot model of GAN, first introduce the research status of GAN; then introduce the theory and framework of GAN, which analyzes in detail why the gradient disappears and the mode collapses during the training of GAN; then discussed some typical GAN improvement models, and summarized their theoretical improvements, advantages, limitations, application scenarios and implementation costs; Finally, the application results of GAN in data generation, image super-resolution, and image style conversion are shown, and the current challenges and future research directions of GAN are discussed.
ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatial-temporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method.
An Augmentation Attention Mechanism for High-Spatial-Resolution Remote Sensing Image Scene Classification

High-spatial-resolution remote sensing (HRRS) image scene classification, which categorizes HRRS images into an independent set of semantic-level land use and land cover classes based on image contents, has attracted much attention, and many methods have been proposed due to its wide application in earth observation tasks. In fact, categories of HRRS images depend on regions containing class-specific ground objects, while most of the existing methods for HRRS image scene classification only focus on global information, which introduces redundant information and results in the poor performance of HRRS image scene classification. To overcome the shortcomings of the existing methods, an attention mechanism-based convolutional neural network with multiaugmented schemes is proposed in this article. In the proposed method, augmentation operations over attention mechanism feature maps are used to force the model to capture class-specific features and eliminate redundant information and push the model to capture discriminative regions as much as possible, instead of using all global information without favor. Moreover, a bilinear pooling is utilized to expand the interclass discrimination. Still, feature center loss motivated by center loss is applied to narrow the intraclass gap. To verify the effectiveness of the proposed end-to-end model, three benchmarks are used for testing, and the experimental results have proven the superiority of the proposed method, compared with current state-of-the-art end-to-end methods for HRRS image scene classification.
Early Detection of Glaucoma Using Residual Networks

Glaucoma is one of the leading causes of irreversible blindness around the world and remains asymptomatic until its later stages. Therefore, early diagnosis is of crucial importance. The detection of glaucoma in early stages (from color fundus images) is a challenging task, since the clinical signs in the retinal images are very subtle and go undetected most of the time by the human eye. Convolutional neural networks have proven to provide good results for automatic detection of subtle features from images. In this work we explore the possibility of using residual networks to detect early stages of glaucoma. We introduce a proprietary early-stage glaucoma fundus color images dataset. We used a ResNet50 network which initially was trained on the ImageNet dataset. The level of accuracy on the validation set was 96.95%. The results indicate that using deep learning algorithms a cost-effective screening tool could be built for early and costeffective detection of glaucoma.
A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy

In the letter, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patient's function limb (FL) to the impaired limb (IL). The IL mimics the action prescribed by the FL with the assistance of the wearable robot, stimulating and strengthening the injured muscles through repetitive exercise. A master-slave robotic system is presented to implement the mirror therapy. Especially, the reinforcement learning is involved in the human-robot interaction control to enhance the rehabilitation efficacy and guarantee safety. Multi-channel sensed information, including the motion trajectory, muscle activation and the user's emotion, are incorporated in the learning algorithm. The muscle activation is expressed via the skin surface electromyography (EMG) signals, and the emotion is shown as the facial expression. The reinforcement learning approach is realized by the normalized advantage functions (NAF) algorithm. Then, a lower extremity rehabilitation robot with magnetorheological (MR) actuators is specially developed. The clinical experiments are carried out using the robot to verify the performance of the framework.
Effective and Fast DeepFake Detection Method Based on Haar Wavelet Transform

DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today’s life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.