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In the present situation human health is a very important factor. With today's growing technology, health conditions are deteriorating day by day. Malnutrition, lifestyle play a very important role. These are leading to many diseases such as respiratory infections, heart disease, diabetes, Cancer, Alzheimer's, strokes and many more. Among these, few diseases are curable, due to the facilities available. Whereas few may be non-curable if care is not taken in the initial stages. Cancer poses the biggest threat among all life-threatening diseases. An efficient lesion segmentation method is introduced for the detection of Melanoma skin cancer disease at the preliminary stages using Adaptive Contour Model (ACM). High-quality segmentation is achieved based on contour features and sharp edge detection using ACM. The performance of the proposed Adaptive Contour Model (ACM) is tested upon PH2 and ISIC Challenge 2017 Dataset. The Performance matrices for the segmentation process are measured in terms of Jaccard index (JA), Dice coefficient (DI) and accuracy that are found to be 79.23, 87.26 and 94.63 considering ISIC dataset and 89.14, 93.98 and 96.95 which is quite high considering PH2 dataset. A method for detection of melanoma has been the critical need of the day. The proposed method shows that the performance of the Adaptive Contour Model (ACM) is more than 95%, which is better than other methods. [ABSTRACT FROM AUTHOR]

The paper presents findings from a study that examined, through the lens of the Image Having layer of the Pirie–Kieren model, the qualitative characteristics of the images that different children formed when engaging with eight, novel partitive quotient tasks. The Image Having layer is the first point of abstraction within the Pirie–Kieren model. Therefore, this research is significant in aiming to advance theoretical insight into how the notion of child-created images relates to the development of children's mathematical understanding, in the context of novel for them tasks. This study adopted a qualitative, microgenetic research design and involved nine Year 5 (aged 9–10 years) children. Data based on children's verbal responses and jottings were collected through multiple trials over eight individual sessions with each child. Analysis of 72 interview transcripts showed that children formed and used a range of different images that varied across tasks but also within the same task. This study provides a nuanced description of qualitative distinctions in the nature of child-created images. It thus reveals varied dimensions of a dynamic process of knowledge development and sense-making. This highlights, for educators, the need to be aware of and adaptive to the varied and dynamic dimensions of knowledge that children draw from, when dealing with novel tasks, and which change as children's understanding of new mathematical content develops. [ABSTRACT FROM AUTHOR]

With the proliferation of the use of the Internet and social media, the digital media industry has grown enormously in recent years. However, this has brought some challenges, including issues of content security and copyright violation. In this paper, we propose a fusion-based secure watermarking algorithm that we have named "FuSIW". This uses a hashing scheme, to guarantee copyright protection and authentication of RGB images. The algorithm uses the non-subsampled contourlet transform (NSCT) to create a fused watermark image. This contourlet transform (CT) and randomised-singular-value-decomposition (RSVD) based approach allows concealment of the encrypted fused watermark image in the blue channel of the cover image. Subsequently, the hash value of the cover image is inserted into the green channels of the host image. Experimental evaluation indicates that the FuSIW algorithm provides security from geometric attacks and several other common forms of attack. Simulations indicate that the proposed system exhibits improved robustness and security compared to existing methods. [ABSTRACT FROM AUTHOR]

In this paper, an image encryption technique is proposed to encrypt the images by using a 2D Logistic map with the SHA-256 sequence generator. The initial conditions and control parameters of the chaotic maps served as the key for the cryptosystem, and were made dependent on the plain image through the SHA-256 protocol. The internal loop of the proposed image encryption method is made of 2D logistic permutation, and 2D logistic diffusion. In order the proposed system performance, widely known and accepted metrics as keyspace, key sensitivity, histogram, correlation and entropy were computed. Experimental results showed that the proposed approach was highly key sensitive and exhibited a good resistance against brute-force and statistical attacks. The cryptosystem presented in this part was designed to ensure the safe exchange of medical images on the 5G telecommunication network using IoHT in the interconnexion of different hospital services. [ABSTRACT FROM AUTHOR]

Technology is becoming more portable, easy to use and helping the human beings in day to day life. Smart wearable devices are widely used in many fields nowadays. Wearables can deliver unique value to users and to others who access the data they collect. Wearable devices are gaining attention in capacities related to processing, sensing, storing and communication. It is the main reason that several publications are available on wearable technology, but still there is no survey manuscript available for camera based wearables. So, this work is focused on the camera based wearable devices and its applications in various areas. In camera based wearable devices, captured images are used as input and user is informed after processing the information. This paper provides classification of camera based wearables, a discussion on architectures, data processing aspects, and market position etc.. Finally, the survey highlights the Challenges, future trends and solutions of camera based wearables. [ABSTRACT FROM AUTHOR]

Crop diseases are one of the primary triggers of yield devastation. As a result, early detection of crop diseases is critical to avert crop losses. In this study, a Deep-Dream (DD) based crop leaf disease detection (CLDD) architecture is proposed using a combination of Deep Learning (DL) along with Machine Learning (ML) techniques. DD is used to pre-process and segment the lesions present in leaves. The proposed novel framework involves 24 different Hybrid Deep Neural (HDN) Models that comprise the integration of eight distinct variations of the pre-trained DL model, namely EfficientNet (EffiNet) ranges from EffiNet B0-B7 in form of a feature extractor. Subsequently, three ML algorithms, namely Random Forest (RF), AdaBoost (ADB), and Stochastic Gradient Boosting (SGB) are employed as classifiers. In this study, the Optuna framework was also applied to tune the hyperparameters of these classifiers. For this implementation, the tomato crop dataset (a subset of the PlantVillage dataset) was used. It has been observed that the proposed model achieved the highest accuracy with the DD-EffiNet-B4-ADB model. The accuracy results of all the models have ranged from 84% to 96%. Moreover, DD shows a better interpretation of the disease lesions, and hence classification accuracy is also enhanced by 3% (93% to 96%) after applying DD segmentation. The proposed model was also validated with an actual-field tomato crop leaf image database gathered from the Indian Agricultural Research Institute with a high-level accuracy of 100%. This technique could help farmers to predict the pathogenic diseases at an early stage of disease visibility. [ABSTRACT FROM AUTHOR]

This research paper analyzes the speckle noise distributions in images for denoising performance prediction through the prism of spatial domain. The values at the maximum, minimum and middle of spectrum in spatial domain are taken as reference values. All obtained results give a better overview of the "nature" of the digital images in comparison to the theoretical definitions of noises and images as digital signals. Therefore, analyses of the noises in the 2D spectrum give good recommendations for improvement of the filters. The main aim in this study is to investigate which speckle noise distributions in images has the strongest influence for denoising performance prediction. The clean images are available and we adopt it for evaluating our network. In our experiments, Peak Signal to Noise Ratio (PSNR), normalized color difference (NCD), and feature similarity index for color image quality assessment (FSIMc), are used to measure denoising performance. is selected as the evaluation index of the image. Studies on speckle noise distributions in images show that such distribution do have certain disciplines. ALOHA filter is the most influential for the denoising performance prediction. [ABSTRACT FROM AUTHOR]

In this study, we aimed at finding differences between event-related potentials (ERPs) associated with creating titles for artistic images different in content and style. We had 36 adolescents (16 males and 20 females aged 15.9 ± 1.1 years) take part in two studies and used images from three categories ("caricatures," "story sketches," and "paintings") full of metaphors and symbols as the stimuli. We considered creating titles for caricatures and story sketches of modern artists as a convergent creative task, whereas creating the titles for paintings was viewed as a more open and divergent creative task. During creating titles for the story sketches vs caricatures, the ERP's amplitude on intervals 116–208 and 492–656 ms was higher in parietal-occipital and frontal, central, and parietal areas of the left hemisphere, respectively. While creating titles for the paintings in the author's style vs realistic paintings differences in the ERP's amplitude were found in occipital areas (152–264 ms), frontal and occipital areas bilaterally (208–440 ms), and in frontal, temporal, central, and parietal areas with maximum in the left hemisphere (544–600 ms). A late positive wave was found in two studies: story sketches vs caricatures (with the latency of 492–656 ms) and author's style vs realistic images (with the latency of 544–600 ms) and may be connected with the extraction and analysis of the metaphorical meaning and symbols in the images for creating the verbal interpretations (titles). Only in performing a convergent creative task (closely related to the interpretation of the artist's idea), finding the answer was accompanied by a smaller amplitude of late differences in the interval of 1300–1650 ms (more than 1500 ms before the moment when the answer was marked as found), whereas there were no significant differences between finding and not finding the answer in the second study. With the same instruction, the authors suggested that in the first study, finding an answer was accompanied by a more pronounced process of comparing one's own ideas with a certain (implied by the artist) meaning, which was expressed in late differences between the conditions for finding and not finding answering, while in the second study, in the context of a divergent task, the process of comparing one's own meaning and inherent one was less pronounced and was not reflected in a number of later differences. [ABSTRACT FROM AUTHOR]

In this paper, I present an analysis of the depictive properties of deepfakes. These are videos and pictures produced by deep learning algorithms that automatically modify existing videos and photographs or generate new ones. I argue that deepfakes have an intentional standard of correctness. That is, a deepfake depicts its subject only insofar as its creator intends it to. This is due to the way in which these images are produced, which involves a degree of intentional control similar to that involved in the production of other intentional pictures such as drawings and paintings. This aspect distinguishes deepfakes from real videos and photographs, which instead have a non-intentional standard: their correct interpretation corresponds to the scenes that were recorded by the mechanisms that produced them, and not to what their producers intended them to represent. I show that these depictive properties make deepfakes fit for communicating information in the same way as language and other intentional pictures. That is, they do not provide direct access to the communicated information like non-intentional pictures (e.g., videos and photographs) do. Rather, they convey information indirectly, relying only on the viewer's assumptions about the communicative intentions of the creator of the deepfake. Indirect communication is indeed a prominent function of such media in our society, but it is often overlooked in the philosophical literature. This analysis also explains what is epistemically worrying about deepfakes. With the introduction of this technology, viewers interpreting photorealistic videos and pictures can no longer be sure of which standard to select (i.e., intentional or non-intentional), which makes misinterpretation likely (i.e., they can take an image to have a standard of correctness that it does not have). [ABSTRACT FROM AUTHOR]

The article discusses the limitations of ChatGPT, an artificial intelligence (AI) language model, in generating anatomical images for medical education. The study found that the AI-generated images lacked anatomical precision, making them unsuitable for clinical and educational purposes. The authors suggest that the development of more specialized AI algorithms, such as Diffusion Models, could lead to more accurate medical illustrations. They emphasize the importance of collaboration between AI researchers and medical professionals to improve the quality and precision of medical education. [Extracted from the article]

Images, such as photographs and diagrams, play an important role in the teaching and learning of science. To optimize student learning, educational science images should be designed to facilitate the cognitive processes relevant to comprehension. One such process is comparison, which involves aligning multiple representations on the basis of their common relational structure. This structural alignment process can be facilitated by cognitive supports that are inherent to an image, including its spatial layout. Yet, little is known about the extent to which students must engage in comparison to learn from science images, and whether widely-used educational materials are conducive to structural alignment. To address these issues, we sampled multiple chapters from each of three popular U.S. middle school life science textbooks. We coded each image for the presence of prompts for comparison using cues within the images and surrounding text. For each image that prompted comparison, we coded whether its layout facilitated relevant structural alignment (direct placement of matched pairs) or obscured alignment (impeded placement). Overall, we found that comparisons were prompted for more than a third of the images. However, fewer than half of the images that required comparison had a spatial layout that provided strong support for comparison—that is, direct placement of matched objects/parts. We propose that, in concert with other cognitive supports for learning from multiple representations, spatial supports for comparison could be applied broadly to increase the effectiveness of educational science images. [ABSTRACT FROM AUTHOR]

The recent proliferation of deepfakes and other digitally produced deceptive representations has revived the debate on the epistemic robustness of photography and other mechanically produced images. Authors such as Rini (2020) and Fallis (2021) claim that the proliferation of deepfakes pose a serious threat to the reliability and the epistemic value of photographs and videos. In particular, Fallis adopts a Skyrmsian account of how signals carry information (Skyrms, 2010) to argue that the existence of deepfakes significantly reduces the information that images carry about the world, which undermines their reliability as a source of evidence. In this paper, we focus on Fallis’ version of the challenge, but our results can be generalized to address similar pessimistic views such as Rini’s. More generally, we offer an account of the epistemic robustness of photography and videos that allows us to understand these systems of representation as continuous with other means of information transmission we find in nature. This account will then give us the necessary tools to put Fallis’ claims into perspective: using a richer approach to animal signaling based on the signaling model of communication (Maynard-Smith and Harper, 2003), we will claim that, while it might be true that deepfake technology increases the probability of obtaining false positives, the dimension of the epistemic threat involved might still be negligible. [ABSTRACT FROM AUTHOR]

With the increased use of smartphones over the years, an immense evolution in camera technology and image enhancement applications has been observed. However, this associates significantly greater security concerns with smartphones. Cyber crime offenders often retrieve personal images from second hand smart phones and try to blackmail or harass their former users. Similar situations are often faced by users in undesirable situations like hacking or theft of smartphones. Despite deletion of images by popular image deletion tools, sensitive images can still be recovered by using widely available forensic methods and tools. This paper checks the efficacy of top five image deletion applications namely Shreddit, Android Eraser, Permanent Delete, Sdelete, and Safe Delete in perspective of permanent deletion of images for Android, against National Institute of Standards and Technology's (NIST), Standard for Media Sanitization known as NIST 800-88. The efficiency of the aforementioned deletion applications has been analyzed by using famous image recovery forensic tools; XRY, Autopsy, and DiskDigger. The results of our forensic analysis recovered the deleted images which were ostensibly deleted by the chosen applications. Moreover, all the image locations have been identified in this research which were found to contain images' artifacts. In addition to that, the capability of forensic tools in recovering deleted images has been discussed too. So, this research highlights weaknesses of existing deletion mechanisms. Moreover, based on the conducted forensic analysis and NIST 800-88 standard, the research proposes a model for secure deletion by setting grounds for the development of a secure image deletion application to make permanent deletion of images possible. [ABSTRACT FROM AUTHOR]

Electrical utility companies around the world are keeping track of all equipment on their distribution grid, because it will help them improve the management and the quality of the services they offer to their customers. Asset management of the electric grid is usually conducted manually, which is expensive, time consuming and the results obtained are often not accurate. In this article an automated asset management system for electricity, transport infrastructures is proposed, it is based on images taken by drones and analysed by Faster Region proposal Convolutional Neural Networks (Faster-RCNN) to generate the inventory. The designs of CNN are inspired from the human brain structures, they have been applied to many fields such as object recognition and crowed counting with promising results that are proven to be better than human observer. In order to evaluate the proposed asset management approach, a sample of images was randomly selected from a given dataset, the inventory results generated by the CNN based model are accurate, faster and cheaper than the previous approach based on human observers and helicopters. [ABSTRACT FROM AUTHOR]

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article's body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world's largest source of encyclopedic knowledge. [ABSTRACT FROM AUTHOR]

Objectification involves treating someone (a subject) as a thing (an object). The role of images in perpetuating objectification has been discussed by feminist philosophers. However, the precise effect that images have on an individual's visual system is seldom explored. Kathleen Stock's work is an exception—she describes certain images of women as causing viewers to develop an objectifying 'gestalt' which is then projected onto real-life women. However, she doesn't specify the level of visual processing at which objectification occurs. In this paper, I propose that images can affect a viewer's early visual system. I will argue that if a viewer is exposed to a lot of images that depict women as sexual objects, this will bias their early visual selection mechanisms in a way that can result in an objectifying way of seeing. This is an important contribution to work on objectification as it incorporates empirical studies on vision and findings from philosophy of mind. It also examines some of the epistemic and moral consequences of objectification occurring at this early visual stage. [ABSTRACT FROM AUTHOR]

Research on emotional processes has been closely related to the use of emotional stimuli, promoting the development of different standardized sets of images. However, some kinds of images that would be relevant in clinical psychology research are not available, especially for small animal phobias. The aim of the present study is to validate a set of animal images that includes images of cockroaches, which are not present in the current sets of images. Two hundred and forty images depicting five types of animals (cockroaches, spiders, snakes, cats, and butterflies) were collected from online public domains. Four hundred and twenty-four participants (72.9% women) took part in the study rating the images in two affective dimensions (i.e., valence and arousal). Cockroach pictures were rated as significantly more unpleasant than pictures of spiders, snakes, butterflies, and cats. Moreover, results revealed that women rated cockroach, spider, and snake pictures as more negative than men did. Also, women in comparison with men rated cockroach images as more arousing. The results highlight the importance of using images of cockroaches, due to their high negative valence, which even exceeds that of snakes and spiders, the unpleasant animals typically used in phobic research. This set of images can be useful in research on small-animal phobias. [ABSTRACT FROM AUTHOR]

The main aspects of ghost polarimetry (GP), a new promising direction in ghost optics that recently is being developed by our group, are discussed. In modern terminology, GP is an intelligent optical imaging system belonging to the class of artificial intelligence systems. The principles of the theory of GP in both classical and quantum light are considered. The methods and schemes of experimental implementation of GP are discussed. The instrument base, which was created in our laboratory for implementing GP in quantum light, includes counters of single photons, a time-to-digital converter, correlated photon sources and a quantum tomograph, is described. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) has been widely used in the fields of aviation, aerospace and die manufacturing due to its ability to produce metal components with arbitrarily complex shapes. However, the instability of SLM process often leads to quality fluctuation of the formed component, which hinders the further development and application of SLM. In situ quality control during SLM process is an effective solution to the quality fluctuation of formed components. However, the basic premise of feedback control during SLM process is the rapid and accurate diagnosis of the quality. Therefore, an in situ monitoring method of SLM process, which provides quality diagnosis information for feedback control, became one of the research hotspots in this field in recent years. In this paper, the research progress of in situ monitoring during SLM process based on images is reviewed. Firstly, the significance of in situ monitoring during SLM process is analyzed. Then, the image information source of SLM process, the image acquisition systems for different detection objects (the molten pool region, the scanned layer and the powder spread layer) and the methods of the image information analysis, detection and recognition are reviewed and analyzed. Through review and analysis, it is found that the existing image analysis and detection methods during SLM process are mainly based on traditional image processing methods combined with traditional machine learning models. Finally, the main development direction of in situ monitoring during SLM process is proposed by combining with the frontier technology of image-based computer vision. [ABSTRACT FROM AUTHOR]

Recently, social media has become a part of daily people's routine. People frequently share images, text, and videos in social media (e.g., Twitter, Snapchat, Facebook, and Instagram). Consequently, there is a demand for an automated method to monitor and analyze the shared social media content. This research developed a method that aims to detect any threat in the images or comments in the shared content. Instagram has gained popularity as the most famous social media website and mobile application for media sharing. Instagram enables users to upload, view, share, and comment on a media post (image or video). There are many unwanted contents in Instagram posts, such as threats, which may cause problems for society and national security. The purpose of this research is to construct a model that can be utilized to classify Instagram content (images and Arabic comments) for threat detection. The model was built using Convolutional Neural Network, which is a deep learning algorithm. The dataset was collected utilizing the Instagram API and search engine and then labeled manually. The model used was retrained on the images and comments training set with the classes of threat and non-threat. The results show that the accuracy of the developed model is 96% for image classification and 99% for comment classification. The result of this research will be useful in tracking and monitoring social media posts for threat detection. [ABSTRACT FROM AUTHOR]

Crack damage is commonly observed for civil structures and infrastructure in service. The recent years have witnessed an excessive utilization of deep learning models for realizing autonomous and machine-vision based crack detection. Given this trend, this paper recognizes two entwined challenges: the preparation of large-scale training data and the detection of simple crack damage amid complex scenes. To address them, a novel data augmentation technique is proposed considering crack characteristics in images for realizing deep transfer learning using very small datasets. Numerical experimentation is conducted based on two types of crack datasets (concrete structures and asphalt pavement), each of which has only tens of images containing complex scenes. When evaluating the performance, a sliding-window based rating scheme is proposed, which is much stricter than the conventional bounding-box based approach. Quantitative performance analysis shows the acceptable performance (e.g., an overall accuracy of 93.81%, an F-2 score of 74.4%, and a very high recall of 91% for the crack detection in concrete images). The result demonstrates the effectiveness of the proposed data augmentation method and the superior transferability if the transfer learning is carried out through a fully fine-tuned training process. [ABSTRACT FROM AUTHOR]

Islands are fascinating study systems for biogeography, allowing researchers to investigate patterns across organisms on a comparable geographical scale. They are also often important for conservation. Here, we present the first bio-inventory of the ant fauna of the Aeolian Islands, a Sicilian volcanic archipelago formed within the last million years. We documented a total of 40 species, including one first record for Italy (Lasius casevitzi). Mitochondrial DNA barcodes were obtained for all 40 taxa sampled on the islands, 13 of which were studied genetically for the first time. Mitochondrial DNA sequences of island specimens were compared with those of conspecific samples from other Aeolian Islands, Sicily and mainland Italy. Standardized photographical documentation of all sequenced specimens is provided. All but one currently recognized species (97.5%) were recovered as monophyletic. Genetic divergence within species ranged up to 12.4% in Pheidole pallidula, although most species had much lower levels of intraspecific divergence. At the scale of the Aeolian Islands, intraspecific genetic divergence varied significantly between subfamilies, with species of the subfamily Myrmicinae showing higher intraspecific divergences than the Formicinae. Comparison of specimens from the Aeolian Islands with conspecific ones from the putative source populations (Sicily and mainland Italy) suggested that the island of Panarea has the genetically most derived myrmeco-fauna among the seven focal Islands. Overall, DNA barcoding is a useful-albeit not perfect-method for classifying poorly studied groups of organisms and ants in particular. [ABSTRACT FROM AUTHOR]

The purpose of this study is to discuss notions of femininity and masculinity in situations of argumentation among Swedish upper-secondary students who are studying Natural Science. The empirical material is drawn from an ethnographic inspired study, where I followed a group of students in all of their science teaching throughout a semester. This study includes three classroom situations where the students are given a task to play roles where they either argue for or against nuclear power and where they are asked to argue for or against genetically engineered organisms. The students also asked to defend the position of one country in negotiations to limit greenhouse gas emissions in an international climate conference. This study will focus on relational situations at the micro level that are related to masculinities and femininities at the macro-level. The results show how the constructions of argumentation in the role playing tasks are based on an economic terminology and rationality, which can be said to represent a masculine approach. In contrast, the discussions that followed the role playing allowed for affective presentations, which are often regarded as feminine. This study discusses how a critical perspective can contribute to the awareness of the logic of these rendered performances. [ABSTRACT FROM AUTHOR]

Despite these measures, HIPAA fails to account for a major emerging gray area - the use of social media images from publicly available profiles (i.e., Facebook, Instagram, Twitter) in peer-reviewed research. Key terms included "plastic surgery", "social media images", "Twitter", "Facebook", "Instagram", and "social media." Keywords: social media; plastic surgery; education; research; images EN social media plastic surgery education research images 449 450 2 05/26/23 20230601 NES 230601 Over the past two decades, the increasing popularity of social media platforms has introduced a novel set of ethical concerns surrounding social media-centered research. [Extracted from the article]

One distinguishing feature in terms of national curriculum revisions is the emphasis on invention education in the subject of technology. This study investigates how South Korean middle school students perceive inventors. A manufactured tool for testing and a checklist for evaluating images of inventors were created to assess drawings of 115 8th grade students. The final 108 surveys (from 52 male and 56 female students) were analyzed, and the results were quite similar to those of prior studies that examined how students picture scientists. This research confirmed that students had stereotypical images of inventors. Stereotypical elements that were not depicted in prior studies were also discovered. For example, tools such as spanners, hammers, and screwdrivers, which are used for building and fixing things, were proposed as symbols of research by students. Overcoming such stereotypical representations requires improving stereotypical images of inventors as they are presented in photos and figures of textbooks, developing and implementing educational programs that introduce relevant images of inventors, and changing teachers' recognition. [ABSTRACT FROM AUTHOR]

Introduction: The decision-making process for students as to which medical schools to apply to is open to many factors and influences. Research has identified several factors which influence prospective students' choice of medical school and career. There is also evidence that websites and prospectuses may be creating potential barriers to widening access. Methods: The websites and prospectuses of 33 medical schools in the United Kingdom were searched for relevant images. These images and the people in them were subjected to inclusion/exclusion criteria. Data about the images and people were recorded so that a content analysis could be performed. The relative proportions were compared with pre-existing data relating to the medical profession and society. Results: From 33 medical schools, 650 images were included, with 1,817 people depicted. The largest group for the assumed roles was 'student', as expected, with 1,423 people (78%). For the overall theme of the image, community placement themed images made up only 2% of images (14) and hospital placement themed images made up 24% (154). Chi-squared goodness of fit showed statistically significant results for most groups of data when comparing ethnicity, the overall theme of the image and assumed specialty group, but not when comparing gender. Discussion: In conclusion, for gender, medical schools are accurately reflecting national data. However, for ethnicity medical schools fail to accurately represent national data, leading to incorrect signalling about the ethnic makeup of their students. Additionally, medical schools are signalling to students a strong preference for hospital-based settings, despite a strong national drive to recruit more general practitioners. [ABSTRACT FROM AUTHOR]

Prior research suggests that viewing still images and iconic gestures depicting concepts facilitates the learning of concrete words in the initial stages of second language (L2) acquisition. To date, however, the effect of viewing iconic gestures and images hasn't been systematically compared to the effect of glosses in the learning and retrieval of concrete words in early stage L2 acquisition. Therefore, it is unclear whether dual coding theory of embodied theories of cognition provide the most accurate account of these effects. This study demonstrates that concrete L2 words learned via viewing still images are recalled better than L2 words learned via viewing iconic gesture and that L1 glosses fail to facilitate L2 word learning in beginning learners. Together, these findings indicate that images facilitate the learning of concrete L2 words above and beyond glosses in learners unfamiliar with the target language, and that glosses are not always necessary for effective L2 word learning. [ABSTRACT FROM AUTHOR]

Early detection of breast tumors, feet pre-ulcers diagnosing in diabetic patients, and identifying the location of pain in patients are essential to physicians. Hot or cold regions in medical thermographic images have potential to be suspicious. Hence extracting the hottest or coldest regions in the body thermographic images is an important task. Lazy snapping is an interactive image cutout algorithm that can be applied to extract the hottest or coldest regions in the body thermographic images quickly with easy detailed adjustment. The most important advantage of this technique is that it can provide the results for physicians in real time readily. In other words, it is a good interactive image segmentation algorithm since it has two basic characteristics: (1) the algorithm produces intuitive segmentation that reflects the user intent with given a certain user input and (2) the algorithm is efficient enough to provide instant visual feedback. Comparing to other methods used by the authors for segmentation of breast thermograms such as K-means, fuzzy c-means, level set, and mean shift algorithms, lazy snapping was more user-friendly and could provide instant visual feedback. In this study, twelve test cases were presented and by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for these twelve cases. It was concluded that lazy snapping was much faster than other methods applied by the authors such as K-means, fuzzy c-means, level set, and mean shift algorithms for segmentation. Graphical abstract Time taken to implement lazy snapping algorithm to extract suspicious regions in different presented thermograms (in seconds). In this study, ten test cases are presented that by applying lazy snapping algorithm, the hottest or coldest regions were extracted from the corresponding body thermographic images. The time taken to see the results varied from 7 to 30 s for the ten cases. It concludes lazy snapping is much faster than other methods applied by the authors. [ABSTRACT FROM AUTHOR]

In this paper, we consider algorithms for synthesizing effective distortion-tolerant features and decision rules to recognize weakly formalized objects on large-format bitmap images. In addition, we analyze the effect of binarization of the initial bitmap description on the quality of subsequent automatic recognition. [ABSTRACT FROM AUTHOR]

Are coastal communities relevant in fisheries management? Starting from what Svein Jentoft has had to say about the topic, we explore the idea that viable fishing communities require viable fish stocks, and viable fish stocks require viable fishing communities. To elaborate and expand on Jentoft’s arguments, first, we discuss values as a key attribute of communities that confer the ability to manage coastal resources. Turning to power, next we explore why fishing communities need to be empowered by having the opportunity to self-manage or co-manage resources. Third, regarding community viability, we make the argument that (1) rebuilding or maintaining viable fishing communities and fish stocks cannot succeed without first dealing with vulnerabilities, and that (2) the dimensions of vulnerability involve increase/decrease in well-being, better/poorer access to capitals, and building/losing resilience. The idea that healthy fishing communities and healthy fish stocks require one another implies a viable system that contains both, a social-ecological system view. The values embedded in communities enable them to manage resources. Thus, managers and policy makers need to imagine healthy fishing communities who take care of resources, and this positive image of communities is more likely than present policies to lead to viable fishing communities as well as viable fish stocks. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) is one of the effective imaging modality in medical sciences that assist in diagnosing various pathologies inside the human body. Despite considerable advancement in acquisition speed, signal to noise ratio and image resolution of computed tomography imaging technology, CT images are still affected by noise and artifacts. A tradeoff between the amount of noise reduced and conservation of genuine image details has to be made in such a way that it enhances the clinically relevant image content. Therefore, noise reduction in medical images is an important and challenging task, as it helps to improve the performance of other image processing procedures such as segmentation or classification to perform better diagnosis by clinicians. Different techniques have been suggested in the literature on denoising of CT images, and each technique has its own presumptions, benefits, and drawbacks. To the best of our knowledge, no survey paper was found in the literature that compares the performance of various denoising techniques for CT images. This study aims to compare the capabilities of several notable and contemporary denoising techniques in the presence of different types of noise present in abdominal CT images. This comparative analysis helps to determine the most suitable denoising technique for practitioners and researchers that can be used in real life scenarios. Furthermore, the advantages and disadvantages of considered denoising methods have also been discussed along with some recommendations for further research in this area. [ABSTRACT FROM AUTHOR]

Experimental studies of emotion usually use datasets of normative emotional pictures to elicit specific emotional responses in human subjects. However, most of these datasets are not annotated with discriminating and reliable emotional tags, having only valence and arousal ratings for each image. Complementing this information with emotional tags would enrich the datasets, by increasing the number of annotated images available and consequently reducing the use of the same images in consecutive studies. This paper describes a multi-label recognizer that combines a Fuzzy approach with a Random Forest classifier to recognize both polarity and discrete emotions elicited by an image, using its valence and arousal ratings. Polarity indicates whether the emotional content of the image is negative, neutral, or positive, whereas emotions provide a more detailed description of the emotional content conveyed by the image. We evaluated our multi-label recognizer using pictures from four existing datasets containing images annotated with emotional content and valence and arousal ratings. Experimental results show that our recognizer is able to identify polarity with a precision of 84.8%, single emotions with 80.7%, and two emotions with 81.1%. Our recognizer can be useful to researchers who want to identify polarity and/or emotions from stimuli annotated with valence and arousal ratings. In particular, it can be used to automatically annotate with emotional tags already existent image datasets, avoiding the costs of manually annotating them with human subjects. [ABSTRACT FROM AUTHOR]

We develop adaptive algorithms for interpolation of multidimensional signals based on a local autoregressive model of signals. Their adaptability is the result of calculation of the model’s parameters for the reading of each signal using estimates of a local autocorrelation function of the compressed signal. Under developing these interpolation algorithms, we do not include excess hierarchical grids of readings. That is the reason why our interpolators are well adapted for use in the framework of a compression method based on hierarchical grid interpolation. Computer simulations on real images confirm that the proposed interpolators allow us to increase a degree of hierarchical compression. [ABSTRACT FROM AUTHOR]

Information on use/land cover change is important for planners and decision makers to implement sustainable use and management of resources. This study was intended to assess the land use land cover (LULC) change in the Koga watershed. The MSS of 1973, TM images of 1986, 1995 and 2011 were used together with survey and demographic data to detect the drivers of land cover changes. The result revealed that a remarkable LULC change occurred in the study area for the past thirty eight years. The area of cultivated and settlement has increased by 7054.6 ha, while, grass and bush lands decreased by 4846.5 and 3376 ha respectively. Wetland also declined from 580.2 ha to 68.3 ha. The growing demand for cultivable land and fuel wood were the major causes to the deterioration of grass and bush lands. Hence, the appropriate land use policy should be employed to sustain available resource in the watershed. [ABSTRACT FROM AUTHOR]

We introduce the Open Affective Standardized Image Set (OASIS), an open-access online stimulus set containing 900 color images depicting a broad spectrum of themes, including humans, animals, objects, and scenes, along with normative ratings on two affective dimensions- valence (i.e., the degree of positive or negative affective response that the image evokes) and arousal (i.e., the intensity of the affective response that the image evokes). The OASIS images were collected from online sources, and valence and arousal ratings were obtained in an online study (total N = 822). The valence and arousal ratings covered much of the circumplex space and were highly reliable and consistent across gender groups. OASIS has four advantages: (a) the stimulus set contains a large number of images in four categories; (b) the data were collected in 2015, and thus OASIS features more current images and reflects more current ratings of valence and arousal than do existing stimulus sets; (c) the OASIS database affords users the ability to interactively explore images by category and ratings; and, most critically, (d) OASIS allows for free use of the images in online and offline research studies, as they are not subject to the copyright restrictions that apply to the International Affective Picture System. The OASIS images, along with normative valence and arousal ratings, are available for download from or . [ABSTRACT FROM AUTHOR]

The dynamics of the construction and perpetuation of gender stereotypes are classic research subjects in social psychology and in the field of educational guidelines in France. The most recent government decree aims to counter stereotypes in schools, by exposing pupils to counter-stereotypes. This study examines the effects of activating counter-stereotypes, versus stereotypes and gender neutral input, on the test performance of female and male fourth-grade students in a mathematical task in a 'real-life' classroom setting. Theoretical implications of social mechanisms to combat gender stereotypes are discussed. [ABSTRACT FROM AUTHOR]

In most archaeological researches, ceramic cathodoluminescence images are conventionally exploited in a descriptive way (paste colour, inclusions). In this study, a new approach was employed: image's RGB histograms were used in order to differentiate several ceramics recently discovered at the archaeological site of San José de Moro located in northern Peru. Cathodoluminescence coupled with chemometric methods appears as a good method to characterize and particularly to produce a systematic classification of complex materials such as archaeological ceramics. Our results were compared with the archaeological and classical background knowledge and this allowed confirming that Mochica Fine Line and Mochica Polychrome were the most similar archaeological groups, at Mochica times, because of the use of the same raw material for production. However, Highland Cajamarca is the most different ceramic group from the other studied ones due to the use of different raw materials from Cajamarca valley located 100 km east of San José de Moro site. [ABSTRACT FROM AUTHOR]

In this article, the most commonly used algorithms for causal search on fMRI data are reviewed and discussed, with particular attention paid to aspects of the algorithms useful for substantive neuroimaging researchers. Classic algorithms, such as PC and GES, as well as more contemporary algorithms, such as IMaGES-LOFS and GIMME, are compared and contrasted. One major difference between algorithms, the use of lagged variables to infer direction vs. the assumption of non-normality to infer direction is discussed, with eye on the impact those choices have on substantive findings. The algorithms share some commonalities as well as differences. For instance, some use lagged variables to assist in ascertaining directionality, whereas others rely on aspects of non-normality in the data to infer directionality. Attention is given to the impact of these choices on the reliability of results. [ABSTRACT FROM AUTHOR]

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This paper will explore one aspect of the relationship between pretence and narratives. I look at proposals about how scripts play guiding roles in our pretend play practices. I then examine the views that mental representations are needed to guide pretend play, reviewing two importantly different pictures of mental guiders: the Propositional Account and the Model Account. Both accounts are individualistic and internalistic; the former makes use of language-like representations, the latter makes use of models, maps and images. The paper will discuss some worries with the notion of mental guiders, and offer some positive suggestions about what else might be playing the guiding role in pretence. I propose that environmental affordances and socially scaffolded engagements provide the basis of pretence guiders (the Interactive Account), and suggest that engagement in narrative practices further frames and allows elaborations of acts of pretend play (the Narrative Account). I take first steps in developing a new embodied, enactive and intersubjective understanding of pretence, showing why it is a viable alternative to the mental representational accounts of pretence. [ABSTRACT FROM AUTHOR]

The METEOSAT Second Generation (MSG3) geostationary satellite main payload is the optical imaging radiometer, the so-called Spinning Enhanced Visible and InfraRed Imager (SEVIRI). With its 12 spectral channels and its high temporal resolution, SEVIRI can offer improved geophysics thematic products measurements. So, the development of a system that can exploit the temporal behavior of the terrestrial disk observations is crucial for these near-real-time applications. Advanced Very High Resolution Radiometer (AVHRR) data from polar-orbiting NOAA19 satellite are acquired and processed here. A ground station is required to collect and follow the temporal, spectral, and space evolution of the treated topic of interest. The main objective of this research is to give the most important directives for developing a simple remote sensing ground station. The system consists of hardware and software parts and gives meaningful results using received daily data from either SEVIRI-MSG3 or AVHRR-NOAA19. Satellite multi-sensor database is created and enriched everyday. A realized autotracker capable of automatic tracking satellites is mounted in our ground station. Based on received images, some acquisition and MSGViewer software processing results such as brightness temperature, Albedo conversions from the raw data, and different red-green-blue combinations between visible and infrared windows are illustrated and discussed. The system can be easily applied for operational research uses. [ABSTRACT FROM AUTHOR]

The topic of this article is conceptions of the history of Europe in the US World History textbooks from 1921 to 2001. It finds the main theme of this history to be the development of modern democracy and science in Western Europe. The USA features as a forerunner and model of democratisation. Compared to the USA, Europe is held up as both same and different, depending on point in time. More recently, much more space is devoted to non-European history; still where Europe is concerned, the basic historical narrative has remained stable over time. This indicates that the image of Europe has long been fixed in this part of American historical culture. [ABSTRACT FROM PUBLISHER]

Imaging, epitomized by computed tomography, continues to provide unprecedented 3D access to granular microstructures at ever-greater resolutions. The non-destructive technique has enabled deep insight into the morphology and behavior of granular materials, in situ and as a function of macroscopic states, e.g., loads. However, a significant bottleneck in this paradigm is that it ultimately yields qualitative 'pictures' of microstructure. Hence, a major challenge is to extract quantitative descriptors of grain-scale processes, e.g., morphological description of particles, kinematics, and spatial interactions. Existing methods, including watershed and burn algorithms, are plagued with limitations related to image resolution and with the inability to sharply identify grain-to-grain contact regions, which is crucial for studying force transmission and strength in granular materials. In this work, we propose a method to overcome these drawbacks. Specifically, a novel way to extract grain topology in particulate materials via level sets is introduced. It is shown that the proposed method can sharply resolve the topology of grain surfaces near to and far from grain-to-grain contact regions with sub-voxel resolution, and is capable of grain extraction directly in three dimensions. The proposed method still relies on traditional techniques for input, but ultimately leads to much improved grain characterization. We validate the approach using three dimensional CT images of highly rounded (Caicos ooid) and highly angular (Hostun sand) natural materials, with excellent results. [ABSTRACT FROM AUTHOR]

An algorithm for forming characteristic segments of digital images using a fractal model is discussed. An approach to detecting uncharacteristic blocks of an image based on analysis of fractal characteristics of self-similarity is proposed. [ABSTRACT FROM AUTHOR]

In recent years, the computer graphics and computer vision communities have devoted significant attention to research based on Internet visual media resources. The huge number of images and videos continually being uploaded by millions of people have stimulated a variety of visual media creation and editing applications, while also posing serious challenges of retrieval, organization, and utilization. This article surveys recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis. It discusses the problems involved, and suggests possible future directions in this emerging research area. [ABSTRACT FROM AUTHOR]