Reviewerly/exHarmony huggingface dataset:
Reversibility Error of Image Interpolation Methods: Definition and Improvements

There is no universal procedure in image processing for evaluating the quality and performance of an interpolation method.In this work, we introduce a new quantity: the reversibility error.For a given image, it measures the error after applying successively a homography close to the identity, a crop (removing boundary artifacts) and the inverse homography.An average over random homographies is made to remove the dependency on the homography.A more precise measurement discarding very high-frequency artifacts is obtained by clipping the spectrum of the difference.We also propose new fine-tuned interpolation methods that are based on the DFT zoom-in and pre-existing (or base) interpolation methods.The zoomed version of an interpolation method is obtained by applying it to the DFT zoom-in of the image.In the periodic plus smooth version of interpolation methods, the non-periodicity is handled by applying the zoomed version to the periodic component and a base interpolation method to the smooth component.In an experimental part, we show that the proposed fine-tuned methods have smaller reversibility errors than their base interpolation methods and that the error is mainly localized in a small high-frequency band.We recommend to use the periodic plus smooth versions of high order B-spline.It is more efficient and provides better results than trigonometric polynomial interpolation.
Spectator Excitement Detection in Small-scale Sports Events

Detection of the excitement of spectators in sports is useful for various applications such as automatic highlight generation and automatic video editing. Therefore, spectator analysis has been widely studied. The two main approaches used for this include holistic and object-based approaches. Holistic approaches have been applied in most previous works, however, they do not work in small-scale games, where there are fewer spectators compared to those of large-scale games. In this work, we propose a method for detecting the state of excitement of spectators in small-scale games using an object-based approach. To evaluate our method, we build our own datasets consisting of both spectator and player videos. Experimental results show that our method outperforms a holistic baseline method and allows excitement detection of individual spectators.
Container Networking Performance Analysis for Large-Scale User Behavior Simulation

Accurately simulating large-scale user behavior is important to improve the similarity between the cyber range and the real network environment.The Linux Container provides a method to simulate the behavior of large-scale users under the constraints of limited physical resources.In a container-based virtualization environment, container networking is an important component.To evaluate the impact of different networking methods between the containers on the simulation performance, the typical container networking methods such as none, bridge, macvlan were analyzed, and the performance of different networking methods was evaluated according to the throughput and latency metrics.The experiments show that under the same physical resource constraints, the macvlan networking method has the best network performance, while the bridge method has the worst performance.This result provides a reference for selecting the appropriate networking method in the user behavior simulation process.
Research on Intelligent Grasping System of Monocular Vision Guided Manipulator

Abstract In order to solve the problem of how a cooperative robot can quickly and accurately locate and grasp the specified target work piece and avoid obstacles autonomously under the condition of man-machine cooperation and the variable position of the object to be grasped, a new method of robot arm grasping under the visual guidance of ROS system was proposed. Using ROS framework and tools, the color threshold algorithm in ROS-Opencv is used to realize the rapid recognition and positioning of grasping targets. Use the ROS-Moveit! The kinematics calculation, motion planning and obstacle avoidance of the manipulator are completed the relationship between the image coordinate system and the robot coordinate system is established by hand eye calibration technology. In addition, ROS tools can be used to obtain information such as the position and speed of each joint during the movement of the mechanical arm. Through experimental testing and data analysis, it is shown that the hand-eye system is accurate in calibration, object positioning, and the robot arm can accurately grasp the target object, which has certain practical application value.
Car-Like Mobile Robot Path Planning in Rough Terrain With Danger Sources

In many practical robot missions, the workspace may have several danger sources, such as enemy strongholds in the battlefield, explosives in fire rescue missions, and dangerous buildings in earthquake rescue missions. In these situations, the workspace is always rough terrain instead of flat ground. To maximize the safety degree of a path, robots should simultaneously find a relative flat path to the goal position and try to keep a safe distance from the danger sources. In this paper, an existing path planning algorithm based on multi-objective particle swarm optimization is used to find the safe paths with minimum risk degree and terrain roughness. First, the robot working environment is transformed into a matrix which presents the terrain roughness of the workspace using the known static environment data. And the path planning problem converts into a mathematical problem. Considering the effects of danger sources and terrain roughness, a multi-objective optimization algorithm is used to search for feasible paths. Finally, two simulation tests are designed using Microsoft Robotics Developer Studio4 and Matlab. Results show the advantages of the proposed algorithm in finding Pareto optimal paths.
Weighted RPCA based Background Subtraction for Automatic Berthing

Automatic berthing system is a kind of environment through the information collection around to find suitable berthing vessels, automatically enter the position control system. In recent years, background modeling, as an effective computer vision technology, has been widely studied to provide a rapid way to detect the ships entering the imaging area. Recently, robust principal component analysis has attracted much attention in the field of image analysis, it can also be applied to background modeling and is more robust than traditional models in diverse scenes. Alternating direction method of multipliers is used to solve this model for its high precision and speed. However, the process is not fast enough, since the singular values are treated equally during the minimization of nuclear norm and it is not consistent with physical meanings. In this paper, a weighted robust principal component analysis based background modeling method for automatic berthing has been proposed, where different weights are assigned to different singular values. Specifically, the designation of weights has been analyzed in detail to acquire a fast and accurate background modeling. Experimental results clearly show that the proposed method outperform the other state-of-the-art ADMM based algorithm in terms of both qualitative and quantitative analysis.
A new histogram PMHT incorporating pixel noise distribution for dim target tracking

The histogram probabilistic multi-hypothesis tracker (H-PMHT) is an attractive multi-target tracking method which directly processes raw sensor images to detect dim targets. In the H-PMHT, the raw sensor images are converted to histograms, and then the histograms are assumed to follow the multinomial distributions parameterized by mixture density functions, in which each mixture component corresponds to a target object or clutter. Combine this measurement model with the expectation-maximization (EM) method, H-PMHT estimates the states of targets and the mixture proportions. Recently, by assuming alternative measurement models based on Poisson distribution and Interpolated Poisson distribution, researchers proposed the Poisson H-PMHT (P-HPMHT) and the Interpolated Poisson PMHT (IPPMHT) to allow for fluctuating target amplitude. However, these methods fail to take distribution information of pixel noise into tracking consideration, which then results the degradation of detection performance. In this paper, we address this problem by modifying the measurement model of IP-PMHT to allow for incorporating statistical information of pixel noise. A key point to achieve this is that Interpolated Poisson follows a thinning property, which means that the energy from clutter can be modeled with a parameterized Interpolated Poisson in the IP-PMHT. We replace the parameterized Interpolated Poisson with a given distribution, which describes the pixel noise, and propose a new tracking method. An important feature of this new method is that it retains the advantages of the H-PMHT, meanwhile naturally incorporates the prior information about pixel noise in target tracking. Through the Monte Carlo simulations, we prove the superiority of this new method in dim target tracking.
On the Emergence of Oscillations in the Evolved Autosteering of a Car on Slippery Roads

One of the important features of autonomous vehicles is their versatility to various traffic situations and road conditions. We explore the feasibility of using genetic programming to develop an adequate auto-steering of a car in slippery road conditions. We also investigate an important emergent property of the best-evolved steering solutions - the steering oscillations - and discuss how these oscillations contribute to the better controllability of the sliding car. We present the limitations and the technical challenges of the realworld implementation of steering oscillations.
Modeling and Analysis of Measurements of Tightly Coupled GPS/SINS Integrated Navigation System

The tightly coupled GPS/SINS integrated navigation system introduces nonlinearity to the measurement equation of the Kalman filter due to the use of raw GPS pseudorange and pseudorange rate measurements. The Extended Kalman Filter (EKF) is a typical method to address the nonlinearity by linearizing the measurements. However, the linearization of the pseudorange rate measurement is incomplete in the conventional measurement equation. To solve this problem, this paper presents a complete linearized pseudorange rate measurement equation and analyzes the role of the corresponding state variables. Besides, one advantage of the tightly coupled GPS/SINS integrated navigation system is that the system can still work when less than four satellites are tracked. Previous literatures propose that the tightly coupled GPS/SINS integrated navigation system with less than four satellites can work normally for a short time. On this basis, this system is further analyzed from the viewpoint of observability and is not considered to work stably for a long time in this paper. The experimental results prove the analysis of the pseudorange rate measurement equation and the tightly coupled GPS/SINS integrated navigation system with less than four satellites.
Quantifying Process Nonclassicality in Bosonic Fields<sup>*</sup>

Nonclassicality of optical states, as a key characteristic of bosonic fields, is a valuable resource for quantum information processing. We investigate the generation of nonclassicality in quantum processes from a quantitative perspective, introduce three information-theoretic measures of nonclassicality for quantum-optical processes based on the Wigner–Yanase skew information and coherent states, and illustrate their physical significance through several well-known single-mode quantum processes.
SeqNet: An Interest Point Detector Based on Unsupervised Learning

To obtain robust, stable and well-defined image features, an excellent interest point detector is necessary. Interest points in images refer to some special points that are useful for subsequent processing tasks, such as edges, comers, textures, blobs, etc. Since the task is important in computer vision, more and more learning-based methods have been proposed. However, most of their methods are built upon hand-crafted methods, and the performance of hand-crafted detectors limits their further development. In this paper, we propose SeqNet, an unsupervised learning network for detecting interest points. We qualitatively and quantitatively show that our method performs better or on-par with baselines.
Deep Dynamic Mixed Membership Stochastic Blockmodel

Latent community models are successful at statistically modeling network data by assigning network entities to communities and modelling entity relations as the relations of their communities. In this paper, we describe the limitation of these models in inferring relations between two communities when the entity relations between these communities are unobserved. We propose a solution to this problem by factorizing the community relations matrix into two community feature matrices, thereby adding a dependency between community relations. We introduce the deep dynamic mixed membership stochastic blockmodel based network (DDBN) to demonstrate the feasibility of such an approach. Our model marries the mixed membership stochastic blockmodel (MMSB) with deep neural networks for rich feature extraction and introduces a temporal dependency in latent features using a long short-term memory unit for dynamic network modeling. We evaluate our model on the link prediction task in static and dynamic networks and find that our model achieves comparable results with state-of-the-art methods.
Encrypted Dataset Collaboration

The past year has seen increasing scrutiny of Smart Cities efforts with regard to privacy. Privacy advocates have criticized Smart City data collection on the whole and critiqued specific city efforts that they feel have crossed a line.
An LSTM-based Trajectory Estimation Algorithm for Non-cooperative Maneuvering Flight Vehicles

In this paper, a learning-based trajectory estimation method is proposed for non-cooperative flight vehicles. First, a parameterized model of a non-cooperative flight vehicle is established in which three different maneuverings, S-shape, spiral and cycloid maneuvering, are considered. Then, an LSTM-based identification algorithm is proposed to obtain maneuvering parameters of the established models. Then, an Extended Kalman filter is used to estimate the trajectory of the flight vehicle by using the identified parameters. Last, simulation experiments are performed to show the proposed method is effective.
On the question of the crisis of domestic library activity: is there light at the end of the tunnel?

The article is devoted to the prospects of development of the national library activity. The purpose of the article is to determine the ways of library construction. For the first time the original methodology for its development analysis on the basis of supply and demand for library services is proposed. The dynamics of changes in the market of library services in the Soviet and post-Soviet periods is presented. It is shown that the decrease in demand for library services is largely due to the obsolescence of library collections and the inability to meet the information needs of readers. The author comes to the conclusion that the vector of library construction should be set by the state and society. If in the Soviet period the emphasis was placed on the interests of the state to form a new “Soviet” person, today it should shift towards the humanization of social relations and the creation of conditions for the creative development of an individual. It is noted that the unfavorable situation in library construction is created by certain demographic and socioeconomic factors. Their compensation should also form the basis of the state library policy.
Implementation of Porter Stemmer Algorithm to Obtain Basic Words in Toba Batak Language Documents with the Two-Level Morphological Method

Abstract Stemming is the process of deciphering words by carrying out affixes to words to get the right base words. In its development stemming has been developed for various languages, including Porter Stemmer. In this study, the stem was used to add Toba Batak language. Decapitation of affixes in the Toba Batak language is difficult because there are essential words that contain affixes and there are essential words that decay when affixed, namely the essential words beginning with the letter’s ‘t’, ‘s’ and ‘p’. To solve this problem by offering the implementation of a porter stemmer algorithm using the two-level morphological method. This stemmer method uses a dictionary of essential words. Each step in the stemmer process will always check the lexicon of the essential words so that the essential words that contain affixes will not be monitored, and if the word is not in the dictionary the basic word will not be stemming so when there are a name people or place names that contain affixes will not be implemented. The use of two levels of morphology was applied to the essential words beginning with the letter’s ‘t’, ‘s’ and ‘p’ to get the right essential words. From the results of testing on 20 Toba Batak language documents, the Porter stemmer algorithm with two levels of morphology can produce an accuracy rate of 87.503% with an average speed of 0.000460798 seconds per word.
A Method to Evaluate Knowledge Resources in Agile Software Development

Background: Organizations adopting Agile Software Development (ASD) use different Knowledge Management (KM) practices to retain and share knowledge. However, it is often the case that knowledge retention is carried out in an ad-hoc way. Aims: In this study, we report our experience from proposing the Knowledge Critically Evaluation Method (KCEM) to evaluate knowledge items (KIT). Our main goal with KCEMs is to support companies to systematically retain knowledge in ASD contexts. Method: We conducted an improvement case study to develop and evaluate KCEM. This research follows the guidelines for technology transfer between industry and academia. The case and unit of analysis is Ericsson, a Swedish company that develops telecommunication solutions. Results: In this paper, we provide initial results of both lab and static validation, enriched by the lessons learned. Conclusions: The preliminary results show that KCEM is easy to understand and use, provides a different perspective on the KIT by visualizing in the criticality chart, and reduces the level of abstraction associated to a knowledge subject area.
Multi-UAV Cooperative Search method for a Moving Target on the Ground or Sea

Aiming at the problem that the moving target on the ground/sea surface is difficult to detect by the traditional multi-UAV cooperative parallel search strategy, a multi-UAV cooperative search strategy with formation shape of V is proposed in this paper. This method improves the probability of detecting the moving target by reducing the coverage blind area of airborne sensor. Firstly, the search strategy is described in detail. Then, the motion model of UAV, the model for sensor detection, the model of target motion and the discriminant model for target detection are respectively established. Finally, the simulation for the multi-UAV cooperative search strategy with formation shape of V is carried out by Matlab/M-file programming. The comparative simulation results between the proposed approach and the traditional multi-UAV parallel search strategy show that the presented search method can improve the probability of detecting the moving target, which confirms the superiority and effectiveness.
Collaborative UAV Surveillance

Autonomous collaborative robotics is a topic of significant interest to groups such as the Air Force Research Lab (AFRL) and the National Aeronautics and Space Administration (NASA). These two groups have been developing systems for the operation of autonomous vehicles over the past several years, but each system has several critical drawbacks. AFRL's Unmanned Systems Autonomy Services (UxAS) supports pathfinding for multiple tasks performed by groups of vehicles, but has no formal verification, very little physical flight time, and no concept of collision avoidance. NASA's Independent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) has collision avoidance, partial formal verification, and thousands of hours of physical flight time, but has no concept of collaboration. AFRL and NASA each wanted to incorporate the features of the other's software into their own, and so the CRoss-Application Translator for Operational Unmanned Systems (CRATOUS) was created. CRATOUS creates a communication bridge between UxAS and ICAROUS, allowing for full feature integration of the two systems. This combined software is the first system that allows for the safe and reliable cooperation of groups of unmanned vehicles.
Design of automatization of trolley cover based on microcontroller AT Mega 8535 as a learning media at ATKP Medan

As an air transportation service and the operation of flight operations, airports must be arranged in an integrated operational system and capable of providing balanced transportation services. Inside the airport there are equipment that can improve service to aircraft passengers, one of which is a trolley. For this reason, maintenance of these trolleys is necessary and even to develop into. The study used experimental methods of integrating AT Mega microcontrollers, and prototype trolley covers connected with servo motors as the main drivers of the gates. From the research is concluded that this prototype has been successfully designed and between the tools can function properly so that the trolley-writing movement is fully controllable. This automatic trolley has a part that functions to open and close the trolley door automatically. This is very important, because this trolley will work on its own or automatically arrive at the final destination, so the door to close the trolley is very necessary to avoid losing passenger goods. With this design, it is expected to be able to improve services within the airport and as a learning media in the field.
Dual Aspect Sentiment Analysis for Opinion Mining

Social media has become a widely used platform for sharing information as well as expressing feelings and emotions. The work demonstrates the method of analyzing public opinion available on social media platforms using Natural Language Processing. The proposed method extracts the major topics discussed in tweets (of data set taken), via Latent Dirichlet Allocation (LDA) topic model and identifies the underlying emotions contained in tweet using the techniques of Sentiment Analysis. The paper uses the corpus of Twitter data on admission process in India to capture the opinions of stakeholders taking part in the process. Tweeter data of Indian Institute of Technology (IIT) admission has been used to conduct the experiment. Major topics discussed in tweets and the fundamental emotions contained are obtained as results.