Visibility Problems Research Articles

Does “Social” Mean “Public”?

Abstract The article presents an analysis of the use of Facebook on the over 400-day-long anti-fracking protest by farmers in the village of Żurawlów in Poland against the global corporation Chevron. Analysis of this case study was used to discuss the deliberative potential of social media and their power in countering hegemonic discourse and providing visibility in the public sphere to actors and arguments marginalized or excluded by the traditional media. The results discuss Facebook's potential for mobilizing and providing identity while emphasizing the problem of visibility in the public sphere, which was key to the inclusion of discourse in public debate. Harnessing emotions and legitimizing minority interests helped create counter-power, while polarization and “homophile acts” against deliberation geared toward arriving at an agreement.

A Survey of the Landscape Visibility Analysis Tools and Technical Improvements.

Visual perception of the urban landscape in a city is complex and dynamic, and it is largely influenced by human vision and the dynamic spatial layout of the attractions. In return, landscape visibility not only affects how people interact with the environment but also promotes regional values and urban resilience. The development of visibility has evolved, and the digital landscape visibility analysis method allows urban researchers to redefine visible space and better quantify human perceptions and observations of the landscape space. In this paper, we first reviewed and compared the theoretical results and measurement tools for spatial visual perception and compared the value of the analytical methods and tools for landscape visualization in multiple dimensions on the principal of urban planning (e.g., complex environment, computational scalability, and interactive intervention between computation and built environment). We found that most of the research was examined in a static environment using simple viewpoints, which can hardly explain the actual complexity and dynamic superposition of the landscape perceptual effect in an urban environment. Thus, those methods cannot effectively solve actual urban planning issues. Aiming at this demand, we proposed a workflow optimization and developed a responsive cross-scale and multilandscape object 3D visibility analysis method, forming our analysis model for testing on the study case. By combining the multilandscape batch scanning method with a refined voxel model, it can be adapted for large-scale complex dynamic urban visual problems. As a result, we obtained accurate spatial visibility calculations that can be conducted across scales from the macro to micro, with large external mountain landscapes and small internal open spaces. Our verified approach not only has a good performance in the analysis of complex visibility problems (e.g., we defined the two most influential spatial variables to maintain good street-based landscape visibility) but also the high efficiency of spatial interventions (e.g., where the four recommended interventions were the most valuable), realizing the improvement of intelligent landscape evaluations and interventions for urban spatial quality and resilience.

Dublă subordonare. Subalternitate în Platforma lui Michel Houellebecq

In this paper we explore the exploitation and the discrimination of the feminine other portrayed in contemporary literature as figure of subalternity in the current neocolonial context. Taking into consideration the logic of modernity and that of coloniality as intrisincally interlinked, and the problematics of speaking and acting as a racialized and, at the same time, sexualized woman in the sex industry, we intend to analyse Michel Houellebecq’s Platform from the vantage point of Subaltern Studies in order to showcase how the feminine condition becomes a problem of consciousness and visibility, and how the exploitation of the feminine other is being deepened in a neocolonialist narrative.

Media Visibility Index as a Tool for Measuring the Presence of Political Parties in the Media: Case of Polish Parliamentary Election Campaign in 2019

The problem of media visibility of politicians and political parties is at the heart of the interest of political communication researchers. Theoretical reflections and empirical studies focus on numerous media presence determinants. At the same time, attempts to identify the media visibility of political actors in specific periods, such as the particularly important for democracy period of the election campaign preceding the parliamentary election, are relatively rare. This study is intended to fill that gap. It proposes a simple research tool in the form of a media visibility index, which makes it possible to calculate and compare the visibility of electoral committees in selected media during the election campaign. The tool was used to determine the visibility of electoral committees taking part in the 2019 parliamentary elections in Poland in two TV news services “Wiadomości” and “Fakty”, and three weekly opinion magazines “Newsweek”, “Polityka” and “Sieci”. The data to calculate the index was gathered through manual content analysis of the studied media conducted six weeks before the day of the parliamentary elections.

Meet: Rack-Level Pooling Based Load Balancing in Datacenter Networks

Datacenter networks enable multiple paths between hosts to provide large bisection bandwidth. It requires load balancers to cope with network uncertainties such as traffic dynamics and topology asymmetry. Existing edge-based load balancing schemes are usually faced with the problem of limited network visibility. This article proposes <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> , a rack-level pooling based load-balancer deployed at the edge that can handle the aformentioned uncertainties. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> utilizes both passive information as well as active probing to comprehensively sense the network conditions with relatively low cost. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> dynamically reroutes flows effectively based on the visibility of the network condition. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> has been tested with extensive flow-level simulations against state-of-the-art load balancers. It outperforms Hermes by up to 10% in the experiments, and outperforms others solutions such as DRILL by up to 50%. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Meet</small> requires no modifications to the switches and is feasible to deploy at the edge.

Omega-Test: A Predictive Early-Z Culling to Improve the Graphics Pipeline Energy-Efficiency.

The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is to determine which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overdraw, which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy; however, a conventional Z-Test only avoids a fraction of it. In this article we present a novel microarchitectural technique, the Omega-Test, to drastically reduce the overdraw on a Tile-Based Rendering (TBR) architecture. Graphics applications have a great degree of inter-frame coherence, which makes the output of a frame very similar to the previous one. The proposed approach leverages the frame-to-frame coherence by using the resulting information of the Z-Test for a tile (a buffer containing all the calculated pixel depths for a tile), which is discarded by nowadays GPUs, to predict the visibility of the same tile in the next frame. As a result, the Omega-Test early identifies occluded parts of the scene and avoids the rendering of non-visible surfaces eliminating costly computations and off-chip memory accesses. Our experimental evaluation shows average EDP savings in the overall GPU/Memory system of 26.4 percent and an average speedup of 16.3 percent for the evaluated benchmarks.

From synthetic to natural — single natural image dehazing deep networks using synthetic dataset domain randomization

Image dehazing methods aim to solve the problem of poor visibility in images due to haze. Techniques proposed for image dehazing in literature focus on image priors, haze lines or data driven statistical models. Variations of the classical methods relying on prior model or haze line model use no-reference image quality metrics to prove their dehazing performance. Recently developed deep learning models rely on huge amounts of hazy, haze-free pairs for training, and uses PSNR and SSIM like image reconstruction metrics to show their performance. These methods perform poorly on no-reference image quality assessments and also dehazes poorly at the depths of the image. These methods though can be optimized for memory usage and are faster. This work presents a deep learning model (Feature Fusion Attention Network) trained on a domain randomized synthetic dataset generated in simulation. The proposed model achieves the highest scores on blind image assessments through the gradient rationing technique for a deep learning-based approach by a significant margin. The images were evaluated on full-reference metrics as well and obtained favorable results. This approach also yields one of the highest edge sharpness obtained after dehazing. The training procedure adopted to obtain significant gains on real-world dehazing, without using any real-world data is also detailed in this paper.

A new end-to-end framework based on non-local network structure and spatial attention mechanism for image rain removal

Image rain removal is an important topic in the field of computer vision. In the rainy environment, the rain will seriously affect the quality of imaging, resulting in image deformation, blur, poor visibility, and other problems. So the outdoor vision system cannot accurately detect object, monitor, and other works. Therefore, how to effectively eliminate the rain-weather interference to imaging has a very important practical value. In the absence of the time series information between frame and frame, the bottleneck problem of image rain removal technology is how to effectively remove multi-density rain-fringes while preserving the detailed structure information of the image background. To solve the above problems, a new image rain removal algorithm based on non-local network structure and spatial attention mechanism is proposed in this paper. Firstly, the position relation information between different pixels is obtained by the non-local operation to obtain the global image representation. Secondly, the spatial attention mechanism is used to recalibrate the global information in the spatial dimension. In other words, the nonlinear modeling is carried out on the channel dimension to gather similar features and useful information. Finally, deconvolution and long-distance residual connection are used to restore the size of the rain-removing image layer by layer. The analysis and experimental results show that the proposed algorithm in this paper is effective in removing rain marks, effectively solves the practical difficulties of removing rain stripes with different rain densities, and preserves the details and edge information of the image well.

Effects of soil surface degradation and vehicle momentum on dust emissions and visibility reduction from unpaved roads

Unpaved roads are built on the basis of soil compaction. Therefore, the operation of vehicles on these roads results in dust emissions. As the number of passing vehicles increases, the soil surface gradually degrades under shearing caused by the tires. This enhances emissions and reduces visibility, increasing health and environmental related issues as well as the risk of collisions.The present study was conducted to quantify the effect of surface degradation on dust emissions from several types of vehicles travelling on different soils and assess the evolution of security risks in the vicinity of unpaved roads.Three types of vehicles (a passenger car, a 4WD vehicle and a truck) have been run on an experimental road covered by four different types of soil particles. Each test consisted in driving one of the vehicles at a given speed (30, 45 or 60 km.h−1) on one of the types of particles previously spread on the road at a given mass per unit area (200, 400 or 600 g.m−2). The mass concentration of resuspended particles was measured from the roadside using an Optical Particle Counter (OPC) while the visibility reduction was simultaneously recorded at the same location. The results show that PM10 emission factors increase linearly with vehicle momentum for vehicles without mud flaps. The presence of such appendices can reduce dust emissions by a factor of 7. Moreover, although not considered in conventional models, the state of degradation of unpaved roads may be a key factor regarding dust emissions. The study has also shown that soils with similar silt content can result in very different emission factors. An emission model has then been developed, based on the clay content of the particles. Indeed, this geotechnical parameter is more relevant to characterize the fine soil propensity to generate dust. PM10 emissions are correlated with the reduction of visibility according to a power law for the truck tests.An interesting finding of this work is the fact that the present results can be extended to earthwork sites where visibility problems require spraying of water on the traffic lanes. Improving the modeling of the mechanisms generating dust lift and the associated safety risks is indeed essential for a more efficient use of water on construction sites.

The influence of formaldehyde removal finishing on fogging characteristics and properties of polyester fabrics

The air quality in the vehicle indoor microenvironment has been of particular public concern because of its harmful effect on the human body. The volatile and semivolatile organic compounds in the vehicle indoor air can condense onto the cooler surface of the windshield, potentially creating a visibility and safety problem for the driver. This paper aimed to prepare multifunctional polyester fabrics with formaldehyde removal properties and low fogging degrees. The formaldehyde removal finishing of polyester fabrics was carried out with different formaldehyde removal agents using the pad-dry-cure method. The influence of formaldehyde removal finishing on the formaldehyde removal efficiency, fogging degree and some related performances of polyester fabrics are studied. The results show that the fogging degree increased (lower than standard values: 2 mg) after finishing with formaldehyde removal agents FK-1, FK-2, FK-3, FK-5, and FK-6. The formaldehyde removal rate of polyester fabrics after finishing was above 75%, and the performances of water repellence, softening behavior, color fastness were maintained. Our approach provides an easy method for use in vehicle indoor air purification systems and the finished fabrics can be applied to various applications such as home, automobile, office, and industry.

Implicit neural refinement based multi-view stereo network with adaptive correlation

In this paper, we propose ACINR-MVSNet, an end-to-end trainable framework with adaptive group-wise correlation and implicit neural depth refinement for multi-view stereo (MVS). Previous learning-based MVS methods have demonstrated their outstanding performance, and most of them estimate depth maps in a coarse-to-fine manner. However, in a commonly used multi-stage cascaded framework, the previous wrong estimation might lead to error propagation. In contrast, we focus on another coarse-to-fine structure, i.e., one-stage MVS architecture followed by refinement modules. Inspired by implicit neural representation, we propose an implicit neural refinement module to refine the coarse depth map. Guided by the corresponding reference image, it can better recover finer details, especially those in boundary areas. To solve the visibility problem in complex scenarios while maintaining efficiency, we propose an adaptive group-wise correlation similarity measure for cost volume construction. Besides, we present a pyramid-based feature extraction network with a repeated top-down and bottom-up structure to gather more context-aware information, which can better meet the challenges in ill-posed regions. This novel feature extractor is also utilized to construct an enhanced Gauss-Newton refinement module for further upsampling and optimizing. Extensive experiments on the DTU, the Tanks & Temples and the BlendedMVS datasets demonstrate the effectiveness and generalization of our approach, which can achieve better or competitive results compared to state-of-the-art methods. The code will be available at https://github.com/BoyangSONG/ACINR-MVSNet .

Commentary on “On the optimal placement of cameras for surveillance and the underlying set cover problem” [Applied Soft Computing, vol. 74, 2019, pages 133–153

In a survey article, Kritter et al. (2019) state that the optimal camera placement problem finds application in photogrammetry among many other research areas. They affirm that it has its roots in the set cover problem, which is one of Karp’s original NP-complete problems. They claim that photogrammetry’s optimal camera placement problem can trivially transform into the set cover problem. This article points out the mistake of such an idea by contrasting the mathematical definitions and goals of these two research areas. The document recalls the bundle adjustment method to achieve an accurate three-dimensional reconstruction. Then, the article recaps the idea of Kritter et al. (2019) that the research community based the analysis of camera placement on the visibility problem. Later, the paper presents network design in photogrammetry as an optimization problem based on a scheme of four design stages/questions that photogrammetrists attempt to satisfy. We finish the document by clarifying why it is not possible to reduce the optimal camera placement to the set cover problem by pointing out the mistakes made by Kritter et al. (2019) in the survey.

Deep learning models for visibility forecasting using climatological data

Low visibility conditions affect safety and traffic operations, leading to adverse scenarios that often result in serious accidents. Due to the complexity and variability associated with modeling weather variables, visibility forecasting remains a highly challenging task and a matter of significant interest for transportation agencies nationwide. Given that the literature on single-step visibility forecasting is very scarce, this study explores the use of deep learning models for single-step visibility forecasting using time series climatological data. Five different deep learning models were developed, trained, and tested using data from two weather stations located in the US state of Florida, which is one of the top states nationwide dealing with low visibility problems. The authors provide discussions of the models’ results and areas for future research.

ПРЕГЛЕД АКТИВНОСТИ СОЦИОЛОШКОГ КЛУБА СРПСКОГ СОЦИОЛОШКОГ ДРУШТВА (ОГРАНАК ССД У НИШУ): РАЗВОЈ И МОГУЋНОСТИ

The paper analyzes the origin and development of the Sociological Club of the Serbian Sociological Society, with a special focus on the work of the Club in the Niš branch. The Sociological Club is recognized as a relevant instrument for achieving the goals of the Society, aimed at promoting sociological knowledge and profession. While the work of the Society has been the subject of previous analyzes, the problem of insufficient connection and involvement of branches has only been sporadically examined. The paper points out the impact of this problem on the development of the Club in the branches. We conducted qualitative analysis of the content of available documentation on the work of the Society, in the editions of the Sociological Review and on the Society’s website. The problem of insufficient visibility of the history of the Club and formalization of the current organization and activities of the Club has been recognized. Significant progress in connecting branches in the last two decades, which was pointed out through the analysis, leaves room for further improvement of the Society’s work through the cooperation of branches, in which we recognize the important role that the Club can play. In order to optimally use the potential of the Club, the possibilities of developing the activities of the Club in the branches, and connecting the branches through the network of coordinators of the Club, were pointed out.

Should Hoffa's fat pad be resected during total knee arthroplasty? A review of literature

Knee osteoarthritis is caused by the degeneration of joint tissues through mechanical loading as well as inflammatory effects. The number of total knee arthroplasties is increased and is expected to further be increased by 2050 compared to 2021. The infrapatellar fat pad, also known as Hoffa's fat pad, is often partially or fully resected during total knee arthroplasty to improve the surgeon's view in the upper tibia as well may reduce the risk of soft tissue involvement, when placing the prosthesis. The purpose of this study is to present the importance and functions of Hoffa's fat pad, as well as the latest data on the two conflicting views, on the removal or not of Hoffa's fat pad in total knee arthroplasty. However, there is not a consensus regarding Hoffa's fat pad resection, as its preservation has been associated with increased postoperative anterior knee pain. Due to the fact, that there are no current official guidelines for this issue, it would be preferable that surgeons try to preserve it, but only if there are no visibility and/or access problems during surgery. The choice of complete or partial resection or preservation is based on the surgeon's preferences, experience as well as patient's needs. More high-quality research and randomized clinical trials are needed for better guidance regarding Hoffa’s fat pad management at total knee replacement

Mutual visibility on grid by asynchronous luminous robots

For a team of autonomous opaque point robots on the plane, the Mutual Visibility problem asks for a distributed algorithm that allows the robots to reposition themselves, without colliding, to a configuration where they all can see each other, i.e., no three of them are collinear. In this paper, we investigate the problem on a grid based terrain where the movements of the robots are restricted only along the grid lines and only to a neighboring grid point in each step. The robots operate in Look-Compute-Move cycles under an asynchronous scheduler. They have no agreement in coordinate system and do not know the total number of robots in the team. We assume the luminous robots model where each robot is equipped with an externally visible light which can assume a constant number of predefined colors. These colors serve both as memory and as a form of communication. We give an algorithm that solves the problem from any initial configuration using 9 colors.

Line segment visibility with sidedness constraints

We study a family of line segment visibility problems, related to classical art gallery problems, which are motivated by monitoring requirements in commercial data centers. Given a collection of non-overlapping line segments in the interior of a rectangle, and a requirement to monitor the segments from one side or the other, we examine the problem of finding a minimal set of point guards. Guards may be placed anywhere in the interior of the rectangle but not on a line segment. We consider combinatorial bounds of problem variants where the problem solver gets to decide which side of the segments to guard or the problem poser gets to decide which side to guard, and many others. We show that virtually all variants are NP-Hard to solve exactly, and then provide heuristics and experimental results to give insight into the associated practical problems. Finally we describe a program for using experiments to guide the search for optimal combinatorial bounds.

Road Accident Scenario: A Spatio-Temporal Analysis of Chhattisgarh State, India

This paper has been carried out the road accident of district wise in Chhattisgarh State, India. The main analysis of this paper is spatio – temporal distribution of road accidents, death, and injured person by vehicles in the state according to month, diurnal, different types of vehicles, roads, and area wise. January is the most accidental, death, and injured month during the year 2018-2020. The diurnal variation of road accidents shown at evening to night time from 6-9 PM is the maximum. At the evening time the most of people have faced low visibility problems and the mainly in cold winter season. The paper has been Analysis of vehicles wise, the two-wheeler is the highest road accident then four-wheelers shows at because maximum people traveling from one place to another place through this vehicles. According to the road, wise, rural, and district roads have been maximum shows in various districts of the Chhattisgarh state. The rural area indicates more accidents as compared to the urban area because 76.76% of people live in the rural area. Road accidents are increasing day by day due to the high growth rate of the population with the rapid increase of registered motor vehicles but road density and road space have not increased. The district wise accident severity indexes have been shown the maximum cases in Kondagaon, Jashpur district, and the high accident risk district in Raipur. Raipur, Bilaspur, and Durg district shows the maximum road accident in the state. All over India faced COVID-19 pandemic situation from March 2020 and shows the minimum road accident and deaths but an injured person is increased. The government urgently takes appropriate action and needs to improve road safety for this worsening situation.

Modeling Spheres in Some Paranormed Sequence Spaces

We introduce a new sequence space hA(p), which is not normable, in general, and show that it is a paranormed space. Here, A and p denote an infinite matrix and a sequence of positive numbers. In the special case, when A is a diagonal matrix with a sequence d of positive terms on its diagonal and p=(1,1,…), then hA(p) reduces to the generalized Hahn space hd. We applied our own software to visualize the shapes of parts of spheres in three-dimensional space endowed with the relative paranorm of hA(p), when A is an upper triangle. For this, we developed a parametric representation of these spheres and solved the visibility and contour (silhouette) problems. Finally, we demonstrate the effects of the change of the entries of the upper triangle A and the terms of the sequence p on the shape of the spheres.

Learning Evaluation: Students’ Perception of Online English Courses with a Focus on Writing and Reading Material

The government issued a policy to conduct the teaching and learning process through online due to the covid-19.The class has experienced a shift in methods which previously made a direct observations to distance observation. Evaluation is one of the important components and stages that must be taken by teachers to determine the effectiveness of learning.The purpose of this study was to find out how students' perceptions (1) problems, (2) Satisfaction and (Effectiveness) as students in Shipbuilding Engineering semester 3 at PoliteknikNegeri Madura for online class with a focus on writing and reading materials.This study used a descriptive research with a quantitative approach due to its relation to the type of data and its analysis. (1) the data obtained shows that 93.3% of students have the main obstacle in taking online English classes is signal problems and 6.7% of students have sound and visibility problems when the class takes place. (2) the data showed that 50% of students choose the sufficient option, 43.3% choose satisfied and the remaining 6.7% choose less satisfied. (3) the data showed that 60% of students choose the quite effective option, 33.3% choose effective and the remaining 6.7% choose less effective.