Candidate Images Research Articles

Uticaj digitalne političke komunikacije podržane neuromarketing metodama na percepciju potrošača prema turističkoj destinaciji

Digital political communication is developing in parallel with the modernization of information and communication technologies. In an effort to design political messages as adequately as possible neuromarketing methods, that have already had astonishing commercial results, are increasingly being applied. These methods make it possible to find hidden subconscious information in the minds of consumers. Experts in the field of political marketing, wanting to communicate with the younger population, are increasingly employing social media influencers and use the benefits of electronic wordof-mouth communication (eWOM). Their posts are generally considered to be a credible source of information and tend to be viral (spread exponentially through the digital environment). Influencers have the power to encourage followers to engage in a discussion of a particular political phenomenon and share such information with other Internet users. Social media influencers can contribute to building a positive image of political candidates by presenting them as charismatic, ordinary people. In some situations, consumers may associate politically motivated communication with a tourist destination, which can have both positive and negative consequences depending on consumer attitudes towards a particular issue. Negative political comments (for example, messages placed in a digital environment by owner of tourist facility can cause a crisis on the website, and lead to a wave of electronic negative word-of-mouth communication (eWOM) and even boycott by consumers, which as a consequence can significantly endanger the company's image.

Improving Gray World Algorithm Guided by Scene Semantics

Gray world algorithm typically shows poor illuminant estimation performance due to scene variations of natural images. To address the above problem, an improved gray world algorithm guided by scene semantics is proposed. Firstly, dense-SIFT descriptors are calculated using grayscale images to avoid the interference of color-biased images, followed by the bag of words (BoW) model to generate unordered visual vocabulary. Secondly, based on the average brightness of these visual words, word frequency histograms are constructed weighted by brightness using the spatial pyramid matching (SPM) algorithm. Thirdly, the scene semantics similarity between images employing the histogram cross kernel function, and retrieve a set of candidate images that similar to the test image is calculated. Finally, after removing the outlier images using the isolated forest algorithm, the pixel statistics distributions of all remaining images are used to adaptively infer and update the fixed assumption of the gray world algorithm. The improved gray world algorithm implements color constancy through illuminant estimation. Experimental results in three publicly available color constancy datasets (ColorChecker, Cube+ and NUS) show that the proposed algorithm outperforms comparable improved gray world algorithms in single camera tests (nearly 20% angular error improvement), at the same time, achieves optimal performance in cross-camera tests.

A Novel Model-Based Defogging Method for Particle Images With Different Fog Distributions

Defogging of particle images is a common task in machine vision-based measurement of particle size distribution (PSD) for granular materials or products. Current defogging methods are challenged by particle images captured in industrial environment, where artificial light is dominant in the imaging process and the fog is often unevenly distributed in the image due to its rapid and random movement. The recovered images may contain serious overexposures and result in failures in PSD measurement. To solve this problem, we propose a novel model-based defogging method for particle images with different fog distributions. First, a physical model is established to describe the image formation in industrial foggy environment with artificial light. Then, an adaptive method is proposed to estimate the range of the key model parameter (light intensity coefficient) and to obtain candidate defogged images. Finally, the fog-free image is generated by fusing the candidate images to reduce overexposures while retaining the image brightness. The proposed method was validated experimentally using iron ore particle images and compared with ten current defogging methods. Results demonstrate that our proposed method is adaptive to different illuminations and different fog contributions. It outperforms other defogging methods in removing unevenly distributed fog from the image and thus greatly improves the measuring accuracy of particle size. The proposed image defogging method can be integrated into machine vision systems for particle-handling processes to facilitate PSD measurement in foggy environment. The sample implementation of the proposed method is available at “ <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhoushuyi/particle-image-defog</uri> .”

Restoring Fluorescence Microscopy Images by Transfer Learning From Tailored Data

In fluorescence microscopy imaging, noise is a very usual phenomenon. To some extent, it can be suppressed by increasing the amount of the photon exposure; however, it is not preferable since this may not be tolerated by the subjected specimen. Thus, a sophisticated computational method is needed to denoise each acquired micrograph, so that they become more adequate for further feature extraction and image analysis. However, apart from the difficulties of the denoising problem itself, one main challenge is that the absence of the ground-truth images makes the data-driven techniques less applicable. In order to tackle this challenge, we suggest to tailor a dataset by handpicking images from unrelated source datasets. Our tailoring strategy involves exploring some low-level view-based features of the candidate images, and their similarities to those of the fluorescence microscopy images. We pretrain and fine-tune the wellknown feed-forward denoising convolutional neural networks (DnCNNs) on our tailored dataset and a very limited amount of fluorescence images, respectively to ensure both the diversity and the content-awareness. The quantitative and visual experimentation show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen source images, and well-approximates to the fluorescence images. Moreover, the combination of the tailored dataset with a few fluorescence data through the use of fine-tuning offers a good balance between the generalization capability and the content-awareness, on the majority of considered scenarios.

Efficient Image Encryption Based on New Substitution Box Using DNA Coding and Bent Function

This paper contributes to creating an unbreakable S-Box based on strong bent function expanded by DNA sequences and investigates and analyzes the strength of the proposed S-Box against major standard criteria and benchmarks such as interpolation attack, algebraic attack, avalanche effect, nonlinearity, and period. The outcome of the tests shows that the proposed S-box has good security, as well as it is passed all the randomness tests. On average, the results had after the tests applied have been come with SAC =0.50122, NL = 112, BIC =103.40625 and an iterative period with its maximum value = 256. The complexity of the proposed S-Box is increased with the getting algebraic expression of 255 terms which implies that the algebraic attack resistance = 2¹⁶⁰. Based on the proposed S-Box, a candidate image enciphering scheme is also suggested to prove the strength of the S-Box. The analysis of the experiments that applied on two modes of images, gray images and RGB ones, supports the scheme&#x2019;s robustness against different differential and statical using standard criteria such as correlation coefficient analysis, information entropy, histogram analysis, unified average change intensity, number of pixels change rate and many others. This enforces the capability of using it in modern-day cryptosystems that utilized in multimedia data exchange.

El Fenómeno del votante ucraniano moderno: esencia, peculiaridades y tendencias de su desarrollo

The article reveals the peculiarities of the modern Ukrainian voter as a special phenomenon in political science. The main objective of the research is to form a portrait of the modern voter based on data from some sociological surveys, as well as to address the emergence and formation of the image of a desirable candidate for the voter. Historical and statistical analysis methods as well as the comparison method were used. In the results they emphasize that in the personality of the voter we will understand a subject who makes a conscious choice of that political figure that he (the voter) considers capable of solving urgent problems of life, both State and of his person. In this regard, the focus is on revealing the moods in modern Ukrainian society, to describe the image of "an ideal candidate" in the eyes of a modern voter. Special attention was paid to personality as an integral element of the socio-political space and the worldview of the political and electoral sphere. It is concluded that the historical context is very important in the formation of the Ukrainian political environment, which makes us glimpse analytically the peculiarities of the meaning of the act of suffrage.

Media Sosial sebagai Strategi Komunikasi Politik Partai Golkar dalam Melakukan Pendidikan Politik

Apart from being a means to communicate and absorb constituents' aspirations, social media can also be used to create a positive image of legislative candidate and political party. The purpose of this study is to analyze the political communication strategy of the Blitar Regency Golkar party through social media as political education on the development of community digital literacy and to uncover and analyze the factors that hinder the political communication strategy used by the party. This study uses descriptive qualitative method with data collection technique in a documentary manner. The result shows that the political communication strategy carried out by the Golkar party in Blitar Regency is to determine the communication components based on the communication concept of Harold D. Laswell, namely communicator, message, media, communicant, and effect. In implementing this strategy, there are internal and external constraints. Internal constraints include the psychological condition of young people and operational funds that are the responsibility of legislative candidate. Meanwhile, external constraints include media coverage of controversial statements by political elites, the formation of negative public opinion, and competition from other political parties.

Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies.

Microwave breast imaging has seen increasing use in clinical investigations in the past decade with over eight systems having being trialled with patients. The majority of systems use radar-based algorithms to reconstruct the image shown to the clinician which requires an estimate of the dielectric properties of the breast to synthetically focus signals to reconstruct the image. Both simulated and experimental studies have shown that, even in simplified scenarios, misestimation of the dielectric properties can impair both the image quality and tumour detection. Many methods have been proposed to address the issue of the estimation of dielectric properties, but few have been tested with patient images. In this work, a leading approach for dielectric properties estimation based on the computation of many candidate images for microwave breast imaging is analysed with patient images for the first time. Using five clinical case studies of both healthy breasts and breasts with abnormalities, the advantages and disadvantages of computational patient-specific microwave breast image reconstruction are highlighted.

Live speech portraits

To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic model which models the head pose distribution of the target person. Upper body motions are deduced from head poses. In the final stage, we generate conditional feature maps from previous predictions and send them with a candidate image set to an image-to-image translation network to synthesize photorealistic renderings. Our method generalizes well to wild audio and successfully synthesizes high-fidelity personalized facial details, e.g., wrinkles, teeth. Our method also allows explicit control of head poses. Extensive qualitative and quantitative evaluations, along with user studies, demonstrate the superiority of our method over state-of-the-art techniques.

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Most of the existing smoke detection methods are based on manual operation, which is difficult to meet the needs of fire monitoring. To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. The preselected area coordinates and the sample image of visual task are used as network learning. During the training process, we use the feature migration method to avoid the lack of smoke data or limited data sources. Finally, a target detection model is obtained, which is strongly related to a specified visual task, and it has well-trained weight parameters. Experimental results show that this method not only improves the detection accuracy but also effectively reduces the false alarm rate. It can not only meet the real time and accuracy of fire detection but also realize effective fire detection. Compared with similar fire detection algorithms, the improved algorithm proposed in this paper has better robustness to fire detection and has better performance in accuracy and speed.

Research of Metric Learning-Based Method for Person Re-Identification by Intelligent Computer Vision Technology

Person re-identification technology aims to establish an efficient metric model for similarity distance measurement of pedestrian images. Candidate images captured by different camera views are ranked according to their similarities to the target individual. However, the metric learning-based method, which is commonly used in similarity measurement, often failed in person re-identification tasks due to the drastic variations in appearance. The main reason for its low identification accuracy is that the metric learning method is over-fitting to the training data. Several types of metric learning methods which differ from each other by the distribution of sample pairs were summarized in this article for analysing and easing the metric learning methods’ over-fitting problem. Three different metric learning methods were tested on the VIPeR dataset. The distributions of the distance of the positive/negative training/test pairs are displayed to demonstrate the over-fitting problem. Then, a new metric model was proposed by combining the thoughts of binary classification and multi-class classification. Related verification experiments were conducted on VIPeR dataset. Besides, the semi-supervised metric learning approach was introduced to alleviate the over-fitting problem. The experimental results reflect gap between training pairs and test pairs in the metric subspace. Therefore, reducing the difference between training data and test data is a promising way to improve the identification accuracy of metric learning method.

Особенности использования социальных сетей в президентских кампаниях 2016 и 2020 г. в США: сравнительный анализ

The purpose of this article is to analyze the experience of using social networks as a political marketing tool in the US presidential elections. This article partially touches upon the global topic of marketization and digitalization of both the political process in general and at the level of the US presidential election. The paper highlights the changing role of social media as a policy tool, which today has become not only a tool for distributing content, but also one of the tools for analyzing data from the electorate. The author explores the possibilities of social networks, their strengths and weaknesses and development prospects in the field of political marketing. The work touches upon the role of social networks in the formation of «Electronic Democracy», their impact on the candidate's image and the relationship with the personalization of politics in the United States. The main method in the article is comparative analysis. The result was the definition of the role, key features of the mentioned social networks in the field of modern politics. A certain theoretical contribution is seen in the argumentation of the following observations: the speed of interaction between the candidate and the voter through social networks has increased, in addition, the area of image-making has been partially «digitalized». It was revealed that technologies of information influence on American voters, which positively influenced the results of the 2016 presidential election for the Republican candidate, lowered D. Trump's ratings during the 2020 elections.

Do high engagement Instagram images influence presidential candidate evaluation? The moderating effect of familiarity

PurposeThis paper aims to examine how exposure to a presidential candidate's high engagement Instagram images influences a citizen's candidate evaluations.Design/methodology/approachData were collected via Amazon MTurk. A 3 × 2 experimental design was employed to test the persuasive effect of exposure of the “most liked” and “most commented on” images of the top four 2016 US presidential primary candidates on a US citizen's candidate evaluation.FindingsResults reveal that highly engaging Instagram images of unfamiliar presidential candidates positively influenced candidate evaluations. However, the same was not true for more well-known presidential candidates.Research limitations/implicationsThis study was not conducted during a live campaign and only examined four of the top 2016 presidential primary candidates.Practical implicationsThe research includes implications for marketers seeking to increase engagement and reach in Instagram marketing campaigns. This study shows that even brief exposure to a highly engaged post involving an unfamiliar person/product on social media can significantly alter evaluations of that person or product.Originality/valueTo the authors' knowledge, no experimental designs have addressed how Instagram posts influence users' political attitudes and behaviors within the political marketing and communications literature.

Politeness principle of assertive act in debate between Donald Trump and Joe Biden

In presidential elections, debates are often held between presidential candidates to find out the arguments, vision and mission, as well as policies that will be carried out by presidential candidates in their country. In expressing statements in their debates, speakers often do not realize the important aspects of language that affect the image of presidential candidates in public, namely the aspect of politeness. In language, the politeness aspect is studied in pragmatic studies which study the speaker's speech intention which depends on the context. This research used descriptive qualitative method which emphasizes the description of the data. In this study, each data was analyzed by the politeness maxims contained in the speech, as well as the assertive function contained in the politeness speech. There are five politeness maxims and there are speeches that violate the politeness maxims contained in the speeches of presidential candidate debates, and there are five speech functions of speeches that contain politeness and violate politeness maxims. Each politeness utterances and violates politeness has a different assertive function. Keywords: Politeness Principle, Assertive Act, Politeness Maxim, Presidential Debate

ESA-VLAD: A Lightweight Network Based on Second-Order Attention and NetVLAD for Loop Closure Detection

Loop closure detection (LCD) is an important portion of Simultaneous Localization and Mapping (SLAM) because of its ability to reduce accumulated position errors. In this letter, we propose a novel loop closure detection algorithm named ESA-VLAD. The crucial part of ESA-VLAD is a redesigned network with EfficientNetB0 as backbone for extracting global features, which integrates a second-order attention module in order to effectively learn the correlations between features within the feature map. A trainable Vector of Local Aggregated Descriptors (NetVLAD) is integrated in the last layer of the network to generate a compact and fixed-length global feature. Knowledge distillation strategy is adopted in training of the proposed network to accelerate the training process. For the global features, Hierarchical Navigable Small World (HNSW) is employed to retrieve the loop closure candidate images. In addition, an efficient geometrical consistency check based on local difference binary (LDB) descriptors is designed to verify loop closure matches. Experiments on several public datasets demonstrate that ESA-VLAD can obtain higher recall rates under 100% precision and less processing time per frame compared to other typical and state-of-the-art methods.

The Effects of “Live,” Authentic, and Emotional Instagram Images on Congressional Candidate Evaluations

This study examines how Instagram’s design and norms influence expectations for political imagery and, subsequently, the effects of these images on electability, vote likelihood, and candidate evaluations. Using the Elaboration Likelihood Model, we propose three norms of Instagram that likely function as heuristic cues and affect the reception of political visual communication on the platform: liveness, authenticity, and emotionality. We experimentally test these visual features on Congressional candidate images, finding some evidence that live, authentic, and emotional images positively influence vote likelihood but negatively impact electability. Results also indicate that live, authentic, and emotional images either have no or negative effects on female candidate evaluations or have no or positive effects on male candidate evaluations.

Edge Location Method for Multidimensional Image Based on Edge Symmetry Algorithm

The most basic feature of an image is edge, which is the junction of one attribute area and another attribute area in the image. It is the most uncertain place in the image and the place where the image information is most concentrated. The edge of an image contains rich information. So, the edge location plays an important role in image processing, and its positioning method directly affects the image effect. In order to further improve the accuracy of edge location for multidimensional image, an edge location method for multidimensional image based on edge symmetry is proposed. The method first detects and counts the edges of multidimensional image, sets the region of interest, preprocesses the image with the Gauss filter, detects the vertical edges of the filtered image, and superposes the vertical gradient values of each pixel in the vertical direction to obtain candidate image regions. The symmetry axis position of the candidate image region is analyzed, and its symmetry intensity is measured. Then, the symmetry of vertical gradient projection in the candidate image region is analyzed to verify whether the candidate region is a real edge region. The multidimensional pulse coupled neural network (PCNN) model is used to synthesize the real edge region after edge symmetry processing, and the result of edge location of the multidimensional image is obtained. The results show that the method has strong antinoise ability, clear edge contour, and precise location.

AN EFFICIENT METHOD FOR HAND GESTURE RECOGNITION USING ROBUST FEATURES VECTOR

Integration of technology for the Fourth Industrial Revolution (IR 4.0) has increased the need for efficient methods for developing dynamic human computer interfaces and virtual environments. In this context, hand gesture recognition can play a vital role to serve as a natural mode of interactive human machine interaction. Unfixed brightness, complex backgrounds, color constraints, dependency on hand shape, rotation, and scale variance are the challenging issues which have an impact on robust performance for the existing methods as per outlined in previous researches. This research presents an efficient method for hand gesture recognition by constructing a robust features vector. The proposed method is performed in two phases, where in the first phase the features vector is constructed by selecting interest points at distinctive locations using a blob detector based on Hessian matrix approximation. After detecting the area of the hand from the features vector, edge detection is applied in the isolated hand followed by edge orientation computation. After this, templates are generated using one and two dimensional mapping to compare candidate and prototype images using adaptive threshold. The proposed research performed extensive experimentation, where a recognition accuracy rate of 98.33% was achieved by it, which is higher as compared to previous research results. Experimental results reveal the effectiveness of the proposed methodology in real time.

Characteristics of new cement-based thermoelectric composites for low-temperature applications

Thermoelectric cement, the mixture of cement and thermoelectric additives, can convert energy between thermal and electrical forms due to the thermoelectric additives. Potentially, they could be the material for building envelopes to harvest waste heat and/or provide space cooling or heating. When there is a significant difference between indoor and outdoor temperatures, the thermoelectric cement can generate electricity using the temperature gradient. And the same material can cool or heat building space via building envelopes with an electrical input. The research aimed to identify and characterize thermoelectric cement candidates for building envelope applications. The additives used in the studied thermoelectric cement candidates include graphite and MnO2. Except for the additives, the study also explored the impact of the two different fabrication methods: wet-mixing and dry-mixing on thermoelectric performance. The images of TE cement candidates taken by scanning electron microscopy and energy dispersive X-ray microscopy visualized the morphology and distribution of additives in the thermoelectric cement composites. The DynaCool Physical Properties Measurement System used in the study simultaneously measured the candidates' thermoelectric properties, including thermal conductivity, electrical conductivity, Seebeck coefficient, and Figure of merit (ZT). The test results showed that the thermoelectric cement with the additives of 10% (weight ratio) graphite and 5% MnO2 has the highest ZT of 6.2 × 10−6 at 350 K. ZT of the thermoelectric cement is even higher to 10−5 orders of magnitude when applying a four-probe electrical resistivity method to account for the contact resistance.

A Three-stage Filtering Approach for Face Recognition

Face recognition has become a crucial topic in recent decades, which offers important opportunities for applications in security surveillance, human-computer interaction, and forensics. However, it poses challenges, including uncontrolled environments, large datasets, and insufficiency of training data. In this paper, a face recognition system is proposed to iron out the above problems with a new framework based on a hashing function in a three-stage filtering approach. At the first stage, candidate subjects are chosen using the Locality-Sensitive Hashing (LSH) function. We employ a voting system to select candidates via disregarding a large number of dissimilar identities considering their local features. At the second stage, a robust image hashing based on Discrete Cosine Transform (DCT) coefficients is used to further refine the candidate images in terms of global visual information. Finally, the test image is recognized among selected identities using other visual information, resulting in further accuracy gains. Extensive experiments on FERET, AR, and ORL datasets show that the proposed method outperforms with a significant improvement in accuracy over the state-of-the-art methods.