Search results for: serious games features

Authors: Sheetla Prasad

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The objective of this study is ‘discovering the real psyche of human beings for prediction of mode, direction and strength of the potential of actions of the individual. The human face was taken as a source of central point to search for the route of real psyche. Analysis of the face architecture (shape and salient features of face) was done by three directional photographs ( 600 left and right and camera facing) of human beings. The shapes and features of the human face were scaled in 177 units on the basis of face–features locations (FFL). The mathematical analysis was done of FFLs by self developed and standardized formula. At this phase, 800 samples were taken from the population of students, teachers, advocates, administrative officers, and common persons. The finding shows that real psyche has two external rings (ER). These ER are itself generator of two independent psyches (manifested and manipulated). Prima-facie, it was proved that micro differences in FFLs have potential to predict the state of art of the human psyche. The potential of psyches was determined by the saving and distribution of mental energy. It was also mathematically proved.

Keywords: face architecture, psyche, potential, face functional ratio, external rings

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Authors: B. Kishore Kumar, Pogula Rakesh, T. Kishore Kumar

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The music analysis is a part of the audio content analysis used to analyze the music by using the different features of audio signal. In music analysis, the first step is to divide the music signal to different sections based on the feature profiles of the music signal. In this paper, we present a music segmentation technique that will effectively segmentize the signal and thresholding technique to remove silence from the percussive sounds produced by percussive instruments, which uses two features of music, namely signal energy and spectral centroid. The proposed method impose thresholds on both the features which will vary depends on the music signal. Depends on the threshold, silence part is removed and the segmentation is done. The effectiveness of the proposed method is analyzed using MATLAB.

Keywords: percussive sounds, spectral centroid, spectral energy, silence removal, feature extraction

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Authors: Parisa Mansour

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The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Shunsuke Aoki, Taku Ri, Tatsuya Yamazaki

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These days, along with the development of e-sports, video games as a competitive sport is attracting attention. But, in many cases, action in the screen does not match the real motion of operation. Inclusiveness of player motion is needed to increase reality and excitement for sports games. Therefore, in this study, the authors propose a method to recognize player motion by using the motion capture technology and develop a virtual basketball game. The virtual basketball game consists of a screen with nine targets, players, depth sensors, and no ball. The players pretend a two-handed basketball shot without a ball aiming at one of the nine targets on the screen. Time-series data of three-dimensional coordinates of player joints are captured by the depth sensor. 20 joints data are measured for each player to estimate the shooting motion in real-time. The trajectory of the thrown virtual ball is calculated based on the time-series data and hitting on the target is judged as success or failure. The virtual basketball game can be played by 2 to 4 players as a competitive game among the players. The developed game was exhibited to the public for evaluation on the authors' university open campus days. 339 visitors participated in the exhibition and enjoyed the virtual basketball game over the two days. A questionnaire survey on the developed game was conducted for the visitors who experienced the game. As a result of the survey, about 97.3% of the players found the game interesting regardless of whether they had experienced actual basketball before or not. In addition, it is found that women are easy to comfort for shooting motion. The virtual game with motion capture technology has the potential to become a universal entertainment between e-sports and actual sports.

Keywords: basketball, motion capture, questionnaire survey, video ga

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Authors: Saied Farham-Nia, Alireza Ghaffari-Hadigheh

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Game theory is a mathematical tool to study the behaviors of a rational and strategic decision-makers, that analyze existing equilibrium in interest conflict situation and provides an appropriate mechanisms for cooperation between two or more player. Game theory is applicable for any strategic and interest conflict situation in politics, management and economics, sociology and etc. Real worlds’ decisions are usually made in the state of indeterminacy and the players often are lack of the information about the other players’ payoffs or even his own, which leads to the games in uncertain environments. When historical data for decision parameters distribution estimation is unavailable, we may have no choice but to use expertise belief degree, which represents the strength with that we believe the event will happen. To deal with belief degrees, we have use uncertainty theory which is introduced and developed by Liu based on normality, duality, subadditivity and product axioms to modeling personal belief degree. As we know, the personal belief degree heavily depends on the personal knowledge concerning the event and when personal knowledge changes, cause changes in the belief degree too. Uncertainty theory not only theoretically is self-consistent but also is the best among other theories for modeling belief degree on practical problem. In this attempt, we primarily reintroduced Expected Utility Function in uncertainty environment according to uncertainty theory axioms to extract payoffs. Then, we employed Nash Equilibrium to investigate the solutions. For more practical issues, Stackelberg leader-follower Game and Bertrand Game, as a benchmark models are discussed. Compared to existing articles in the similar topics, the game models and solution concepts introduced in this article can be a framework for problems in an uncertain competitive situation based on experienced expert’s belief degree.

Keywords: game theory, uncertainty theory, belief degree, uncertain expected value, Nash equilibrium

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Authors: Jimmylen Tonio, Ali Anudin, Rochelle Irene G. Lucas

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Alongside the progress of electronic-business websites, consumers have become more comfortable with online shopping. It has become customary for consumers that prior to purchasing a product or availing services, they consult online reviews info as bases in evaluating and deciding whether or not they should push thru with their procurement of the product or service. Subsequently, after purchasing, consumers tend to post their own comments of the product in the same e-business websites. Because of this, product reviews (PRS) have become an indispensable feature in online businesses equally beneficial for both business owners and consumers. This study explored the linguistic features and motivations of online product reviews on selected Philippine online shops, LAZADA and SHOPEE. Specifically, it looked into the lexical features of the PRs, the factors that motivated consumers to write the product reviews, and the difference of lexical preferences between male and female when they write the reviews. The findings revealed the following: 1. Formality of words in online product reviews primarily involves non-standard spelling, followed by abbreviated word forms, colloquial contractions and use of coined/novel words; 2. Paralinguistic features in online product reviews are dominated by the use of emoticons, capital letters and punctuations followed by the use of pictures/photos and lastly, by paralinguistic expressions; 3. The factors that motivate consumers to write product reviews varied. Online product reviewers are predominantly driven by venting negative feelings motivation, followed by helping the company, helping other consumers, positive self-enhancement, advice seeking and lastly, by social benefits; and 4. Gender affects the word frequencies of product online reviews, while negation words, personal pronouns, the formality of words, and paralinguistic features utilized by both male and female online product reviewers are not different.

Keywords: lexical choices, motivation, online shop, product reviews

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Authors: Charles W. MacQuarrie, Rachel R. Tatro Duarte

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This paper explores the representation of medieval identity within the world of games such as Dragon Age, Elden Ring, Hellblade: Senua’s sacrifice, fantasy role-playing games that draw effectively and problematically on Celtic and Norse mythologies. Focusing on tattoos, onomastics, and accent as visual and oral markers of status and ethnicity, this study analyzes how the game's interplay between mythology, character narratives, and visual storytelling enriches the themes and offers players an immersive, but sometimes baldly ahistorical, connection to ancient mythologies and contemporary digital storytelling. Dragon Age is a triple a game series, Hellblade Senua’s Sacrifice, and Elden Ring of gamers worldwide with its presentation of an idealized medieval world, inspired by the lore of Celtic and Norse mythologies. This paper sets out to explore the intricate relationships between tattoos, accent, and character narratives in the game, drawing parallels to themes,heroic figures and gods from Celtic and Norse mythologies. Tattoos as Mythic and Ethnic Markers: This study analyzes how tattoos in Dragon Age visually represent mythological elements from both Celtic and Norse cultures, serving as conduits of cultural identity and narratives. The nature of these tattoos reflects the slave, criminal, warrior associations made in classical and medieval literature, and some of the episodes concerning tattoos in the games have either close analogs or sources in literature. For example the elvish character Solas, in Dragon Age Inquisition, removes a slave tattoo from the face of a lower status elf in an episode that is reminiscent of Bridget removing the stigmata from Connallus in the Vita Prima of Saint Bridget Character Narratives: The paper examines how characters' personal narratives in the game parallel the archetypal journeys of Celtic heroes and Norse gods, with a focus on their relationships to mythic themes. In these games the Elves usually have Welsh or Irish accents, are close to nature, magically powerful, oppressed by apparently Anglo-Saxon humans and Norse dwarves, and these elves wear facial tattoos. The Welsh voices of fairies and demons is older than the reference in Shakespeare’s Merry Wives of Windsor or even the Anglo-Saxon Life of Saint Guthlac. The English speaking world, and the fantasy genre of literature and gaming, undoubtedly driven by Tolkien, see Elves as Welsh speakers, and as having Welsh accents when speaking English Comparative Analysis: A comparative approach is employed to reveal connections, adaptations, and unique interpretations of the motifs of tattoos and narrative themes in Dragon Age, compared to those found in Celtic and Norse mythologies. Methodology: The study uses a comparative approach to examine the similarities and distinctions between Celtic and Norse mythologies and their counterparts in video games. The analysis encompasses character studies, narrative exploration, visual symbolism, and the historical context of Celtic and Norse cultures. Mythic Visuals: This study showcases how tattoos, as visual symbols, encapsulate mythic narratives, beliefs, and cultural identity, echoing Celtic and Norse visual motifs. Archetypal Journeys: The paper analyzes how character arcs mirror the heroic journeys of Celtic and Norse mythological figures, allowing players to engage with mythic narratives on a personal level. Cultural Interplay: The study discusses how the game's portrayal of tattoos and narratives both preserves and reinterprets elements from Celtic and Norse mythologies, fostering a connection between ancient cultures and modern digital storytelling. Conclusion: By exploring the interconnectedness of tattoos and character narratives in Dragon Age, this paper reveals the game series' ability to act as a bridge between ancient mythologies and contemporary gaming. By drawing inspiration from Celtic heroes and Norse gods and translating them into digital narratives and visual motifs, Dragon Age offers players a multi-dimensional engagement with mythic themes and a unique lens through which to appreciate the enduring allure of these cultures.

Keywords: comparative analysis, character narratives, video games and literature, tattoos, immersive storytelling, character development, mythological influences, Celtic mythology, Norset mythology

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Authors: Tuti Haryati Jasimin, Hishamuddin Mohd Ali

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Malaysia’s green building development is gaining momentum and green buildings have become a key focus area especially within the commercial sector with the encouragement of government legislation and policy. Due to the emerging awareness among the market players’ views of the benefits associated with the ownership of green buildings in Malaysia, there is a need for valuers to incorporate consideration of sustainability into their assessments of property market value to ensure the green buildings continue to increase in the market. This paper analyses the valuers’ current perception on the valuation practices with regard to the green issues in Malaysia. The study was based on a survey of registered real estate valuers and the experts whose work related to valuation in the Klang Valley area to rate their view regarding the perception on valuation of green building. The findings present evidence that even though Malaysian valuers have limited knowledge of green buildings, they recognize the importance of incorporating the green features in the valuation process. The inclusion of incorporating the green features in valuations in practice was hindered by the inadequacy of sufficient transactional data in the market. Furthermore, valuers experienced difficulty in identifying what are the various input parameters of green building and how to adjust it in order to reflect the benefit of sustainability features correctly in the valuation process. This paper focuses on the present challenges confronted by Malaysian valuers with regards to incorporating the green features in their valuation.

Keywords: green commercial office building, Malaysia, valuers’ perception, valuation, commercial sector

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Authors: Vincenzo Capalbo, Gianpaolo Ghiani, Emanuele Manni

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Global e-commerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the ’20s, new challenges will be faced by companies when planning and controlling e-commerce logistics. In this paper, we survey the related literature on Optimization and Machine Learning as well as on combined methodologies. We also identify the distinctive features of next-generation planning algorithms - namely scalability, model-and-run features and learning capabilities - that will be fundamental to cope with the scale and complexity of logistics in the next decade.

Keywords: e-commerce, hardware acceleration, logistics, machine learning, mixed integer programming, optimization

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Authors: Tsuneyuki Namekata, Yoko Namekata

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It is known that the mean investment evolves from a very low initial value to some high level in the Continuous Prisoner's Dilemma. We examine how the cooperation level evolves from a low initial level to a high level in our Demographic Multi-level Donor-Recipient situation. In the Multi-level Donor-Recipient game, one player is selected as a Donor and the other as a Recipient randomly. The Donor has multiple cooperative moves and one defective move. A cooperative move means the Donor pays some cost for the Recipient to receive some benefit. The more cooperative move the Donor takes, the higher cost the Donor pays and the higher benefit the Recipient receives. The defective move has no effect on them. Two consecutive Multi-level Donor-Recipient games, one as a Donor and the other as a Recipient, can be viewed as a discrete version of the Continuous Prisoner's Dilemma. In the Demographic Multi-level Donor-Recipient game, players are initially distributed spatially. In each period, players play multiple Multi-level Donor-Recipient games against other players. He leaves offspring if possible and dies because of negative accumulated payoff of him or his lifespan. Cooperative moves are necessary for the survival of the whole population. There is only a low level of cooperative move besides the defective move initially available in strategies of players. A player may modify and expand his strategy by his recent experiences or practices. We distinguish several types of a player about modification and expansion. We show, by Agent-Based Simulation, that introducing only the modification increases the emergence rate of cooperation and introducing both the modification and the expansion further increases it and a high level of cooperation does emerge in our Demographic Multi-level Donor-Recipient Game.

Keywords: agent-based simulation, donor-recipient game, emergence of cooperation, spatial structure, TFT, TF2T

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Authors: Vishnu Goyal, Basant Agarwal

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Sentiment analysis research provides methods for identifying the people’s opinion written in blogs, reviews, social networking websites etc. Sentiment analysis is to understand what opinion people have about any given entity, object or thing. Sentiment analysis research can be broadly categorised into three types of approaches i.e. semantic orientation, machine learning and lexicon based approaches. Feature selection methods improve the performance of the machine learning algorithms by eliminating the irrelevant features. Information gain feature selection method has been considered best method for sentiment analysis; however, it has the drawback of selection of threshold. Therefore, in this paper, we propose a hybrid feature selection methods comprising of information gain and proposed feature selection method. Initially, features are selected using Information Gain (IG) and further more noisy features are eliminated using the proposed feature selection method. Experimental results show the efficiency of the proposed feature selection methods.

Keywords: feature selection, sentiment analysis, hybrid feature selection

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Authors: Felipe Aguirre

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In this paper some of the relevant features of the πλῆκτρον within GrecoLatin tradition will be analyzed. Starting from the formal aspects (shape, materials, technical properties) and the archaeological evidence, some of its symbolic implications that emerge in the light of literary and iconographic analysis will be discussed. I shall expose that, in addition to fulfilling a purely physical function within the process of sound production, the πλῆκτρον was the object of a rich imaginery that provided it with an allegorical, metaphorical-poetic and even metaphysical dimension.

Keywords: musicology, ethnomusicology, ancient greek music, plectrum, stringed instruments

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Authors: Nurtazin Sabir, Begon Michael, Yeszhanov Aidyn, Alexander Belyaev, Hughes Nelika, Bethany Levick, Salmurzauly Ruslan

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This paper describes the features of the distribution of the most abundant species of fleas that are carriers of the most dangerous infections in the Balkhash-Alakol depression of Kazakhstan. We show that of 153 species of fleas described in the territory of the great gerbil (Rhombomys opimus Licht.), 35 species are parasitic. 21 of them are specific to gerbils species, and four species of fleas from the Xenopsylla genus are dominant in number and value of epizootic. We also describe the modern features of habitats of these species and their relationship with the great gerbil populations found in the South Balkhash region. It indicates the need for research on the population structure of the most abundant fleas species and their relationship with the structure of the populations of main carrier of transmission infections in the region-great gerbil.

Keywords: Balkhash-Alakol depression, natural foci of plague, species diversity and distribution of fleas, flea and great gerbil population structure, epizootic activity, mass species of fleas

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Authors: Zaynab El Khattabi, Youness Tabii, Abdelhamid Benkaddour

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Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research.

Keywords: video summarization, static summarization, video skimming, semantic features

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Authors: Petr Vaculik

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The newest semiconductor devices on the market are MOSFET transistors based on the silicon carbide – SiC. This material has exclusive features thanks to which it becomes a better switch than Si – silicon semiconductor switch. There are some special features that need to be understood to enable the device’s use to its full potential. The advantages and differences of SiC MOSFETs in comparison with Si IGBT transistors have been described in first part of this article. Second part describes driver for SiC MOSFET transistor and last part of article represents SiC MOSFET in the application of buck converter (step-down) and design of simple RC snubber.

Keywords: SiC, Si, MOSFET, IGBT, SBD, RC snubber

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Authors: Khayrullina Yulia Sergeevna, Tokareva Goulsine Shavkatovna

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The article analyzes the modern major railway hubs as a main part of the Urban Nodal Area (UNA). The term was introduced into the theory of urban planning at the end of the XX century. Tokareva G.S. jointly with Gutnov A.E. investigated the structure-forming elements of the city. UNA is the basic unit, the "cell" of the city structure. Specialization is depending on the position in the frame or the fabric of the city. This is related to feature of its organization. Spatial and functional features of UNA proposed to investigate in this paper. The base object for researching are railway hubs as connective nodes of inner and extern-city communications. Research used a stratified sampling type with the selection of typical objects. Research is being conducted on the 14 railway hubs of the native and foreign experience of the largest cities with a population over 1 million people located in one and close to the Russian climate zones. Features of the organization identified in the complex research of functional and spatial characteristics based on the hypothesis of the existence of dual characteristics of the organization of urban nodes. According to the analysis, there is using the approximation method that enable general conclusions of a representative selection of the entire population of railway hubs and it development’s area. Results of the research show specific ratio of functional and spatial organization of UNA based on railway hubs. Based on it there proposed typology of spaces and urban nodal areas. Identification of spatial diversity and functional organization’s features of the greatest railway hubs and it development’s area gives an indication of the different evolutionary stages of formation approaches. It help to identify new patterns for the complex and effective design as a prediction of the native hub’s development direction.

Keywords: urban nodal area, railway hubs, features of structural, functional organization

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Authors: Alex Arghya Adhikari

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Gaming and esports industry is one of the biggest and fastest growing industries in the world. Globally people have started investing more in this industry since now people believe just like traditional sports, esports can also sustain their future. Last year in the month of December, the Indian government also recognised esports as an official sport but there has not been any positive initiative by the government in encouraging people to enter esports. This is a problem which cannot be overlooked since we are already in the digital age and gaming and esports is the future industry. There is a need for multiple effective marketing strategies by the game publishers to stabilize the esports in the country. Purpose: To observe the marketing-communication strategies that are implemented by Riot Games’ Valorant and how those strategies influence the consumer behavior and the esports of the game. Methodology: Activities over the internet related to the game like livestreams, discord chats, Instagram posts and YouTube videos over a period of two months have been collected through the Digital Ethnography. To support and validate the observations of the data collected, in-depth online interviews have been conducted which includes streamers, journalists, LAN experienced players and casual players. Findings: The game publisher through its Dynamic Competitive Gaming Experience and Community-Engaged Ecosystem succeeded in making the game a Recreational activity and a Community which goes beyond the In-game experiences which helped in understanding the impact of audience engagement on esports and the loopholes and setbacks of Indian esports. Conclusion: The study provides a comprehensive analysis of how Valorant's successful marketing and community engagement strategies have contributed to its global popularity and competitive esports environment. It highlights the various strategies employed by Riot Games to keep players engaged and connected, and also the challenges in the Indian esports landscape which differentiates it from the global competition.

Keywords: esports, valorant, marketing, consumer behaviour

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Authors: W. S. Besbas, M. A. Artemi, R. M. Salman

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Content based face sketch retrieval can be used to find images of criminals from their sketches for 'Crime Prevention'. This paper investigates the problem of CBIR of face sketch images in transform domain. Face sketch images that are similar to the query image are retrieved from the face sketch database. Features of the face sketch image are extracted in the spectrum domain of a selected transforms. These transforms are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Walsh Hadamard Transform (WHT). For the performance analyses of features selection methods three face images databases are used. These are 'Sheffield face database', 'Olivetti Research Laboratory (ORL) face database', and 'Indian face database'. The City block distance measure is used to evaluate the performance of the retrieval process. The investigation concludes that, the retrieval rate is database dependent. But in general, the DCT is the best. On the other hand, the WHT is the best with respect to the speed of retrieving images.

Keywords: Content Based Image Retrieval (CBIR), face sketch image retrieval, features selection for CBIR, image retrieval in transform domain

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Sohni Siddiqui, Anja Schultze-Krumbholz

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Our lives now revolve more around online-related tasks, as the internet has become a necessity. One of the disturbance concerns with high internet usage is the multiplication of cyber-associated risky behaviors such as cyber aggression and/or cyberbullying. Cyber Bullying is an emerging issue that needs immediate attention from many stakeholders such as parents, doctors, school administrators, policymakers, researchers, and others, especially in the COVID-19 pandemic when online learning has been adopted as an instructional strategy, and there is a continuous rise in cyberbullying cases. The aim of the article is to review existing successful and emerging interventions designed to control bullying and cyberbullying by engaging individuals through teachers’ professional development and adopting a whole-school approach. The study identified the strengths and limitations of the programs and suggested improvements to existing interventions. Preparing interventions with a strong theoretical framework, integrating applications of emerging theories in interventions, promoting proactive and reactive strategies in combination, beginning with the baseline needs assessment surveys, reducing digital time and digital divide among parents and children, promoting the concept of lead trainer, peer trainer, and hot spots, focusing on physical activities, use of landmarks are some of the recommendations proposed by authors. In addition to face-to-face intervention, the researchers recommend updating and improving previous intervention programs with games and apps. Especially in the time of pandemic crises, when face-to-face interactions are limited and cyberbullying is triggered, the use of apps, web-based interventions, and games can be an effective way to control electronic perpetration and victimization.

Keywords: anti bullying programs, cyber bullying, individualized trainings, teachers’ professional development, whole school interventions

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Authors: Arsalan Ansari, Muhammad Dawood Idrees, Maria Hafeez

Abstract:

Use of mobile phones has been increasing considerably over the past decade. Currently, it is one of the main sources of communication and information. Initially, mobile phones were limited to calls and messages, but with the advent of new technology smart phones were being used for many other purposes including video games. Despite of positive outcomes, addiction to video games on mobile phone has become a leading cause of psychological and physiological problems among many people. Several researchers examined the different aspects of behavior addiction with the use of different scales. Objective of this study is to examine any distinction between mobile game addicted and non-addicted players with the use of electroencephalography (EEG), based upon psycho-physiological indicators. The mobile players were asked to play a mobile game and EEG signals were recorded by BIOPAC equipment with AcqKnowledge as data acquisition software. Electrodes were places, following the 10-20 system. EEG was recorded at sampling rate of 200 samples/sec (12,000samples/min). EEG recordings were obtained from the frontal (Fp1, Fp2), parietal (P3, P4), and occipital (O1, O2) lobes of the brain. The frontal lobe is associated with behavioral control, personality, and emotions. The parietal lobe is involved in perception, understanding logic, and arithmetic. The occipital lobe plays a role in visual tasks. For this study, a 60 second time window was chosen for analysis. Preliminary analysis of the signals was carried out with Acqknowledge software of BIOPAC Systems. From the survey based on CGS manual study 2010, it was concluded that five participants out of fifteen were in addictive category. This was used as prior information to group the addicted and non-addicted by physiological analysis. Statistical analysis showed that by applying clustering analysis technique authors were able to categorize the addicted and non-addicted players specifically on theta frequency range of occipital area.

Keywords: mobile game, addiction, psycho-physiology, EEG analysis

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Authors: Yeseul Park

Abstract:

Biopolymers produced by organisms highly contribute to the production of metal sulfides, both in extracellular and intracellular biomineralization. We discovered a new type of intracellular biomineral composed of copper sulfide in the periplasm of a sulfate-reducing bacterium. We suggest that the structural features of biomineral composed of 1-2 nm subgrains are based on biopolymer-based capping agents and an organic compartment. We further compare with other types of metal sulfide biominerals.

Keywords: biomineralization, copper sulfide, metal sulfide, biopolymer, capping agent

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

Abstract:

Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: C. V. Aravinda, H. N. Prakash

Abstract:

In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Christian Andrä, Pauline Hähner, Sebastian Ludyga

Abstract:

Introduction: In the recent past, discussions on the importance of physical activity for child development have contributed to a growing interest in executive functions, which refer to cognitive processes. By controlling, modulating and coordinating sub-processes, they make it possible to achieve superior goals. Major components include working memory, inhibition and cognitive flexibility. While executive functions can be trained easily in school children, there are still research deficits regarding the trainability during preschool age. Methodology: This quasi-experimental study with pre- and post-design analyzes 23 children [age: 5.0 (mean value) ± 0.7 (standard deviation)] from four different sports groups. The intervention group was made up of 13 children (IG: 4.9 ± 0.6), while the control group consisted of ten children (CG: 5.1 ± 0.9). Between pre-test and post-test, children from the intervention group participated special games that train executive functions (i.e., changing rules of the game, introduction of new stimuli in familiar games) for ten units of their weekly sports program. The sports program of the control group was not modified. A computer-based version of the Eriksen Flanker Task was employed in order to analyze the participants’ inhibition ability. In two rounds, the participants had to respond 50 times and as fast as possible to a certain target (direction of sight of a fish; the target was always placed in a central position between five fish). Congruent (all fish have the same direction of sight) and incongruent (central fish faces opposite direction) stimuli were used. Relevant parameters were response time and accuracy. The main objective was to investigate whether children from the intervention group show more improvement in the two parameters than the children from the control group. Major findings: The intervention group revealed significant improvements in congruent response time (pre: 1.34 s, post: 1.12 s, p<.01), while the control group did not show any statistically relevant difference (pre: 1.31 s, post: 1.24 s). Likewise, the comparison of incongruent response times indicates a comparable result (IG: pre: 1.44 s, post: 1.25 s, p<.05 vs. CG: pre: 1.38 s, post: 1.38 s). In terms of accuracy for congruent stimuli, the intervention group showed significant improvements (pre: 90.1 %, post: 95.9 %, p<.01). In contrast, no significant improvement was found for the control group (pre: 88.8 %, post: 92.9 %). Vice versa, the intervention group did not display any significant results for incongruent stimuli (pre: 74.9 %, post: 83.5 %), while the control group revealed a significant difference (pre: 68.9 %, post: 80.3 %, p<.01). The analysis of three out of four criteria demonstrates that children who took part in a special sports program improved more than children who did not. The contrary results for the last criterion could be caused by the control group’s low results from the pre-test. Conclusion: The findings illustrate that inhibition can be trained as early as in preschool age. The combination of familiar games with increased requirements for attention and control processes appears to be particularly suitable.

Keywords: executive functions, flanker task, inhibition, preschool children

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Authors: Zebiniso Mukhsinova

Abstract:

This article discusses the dynamics of the historical development of the Bukhara school of copper-stamped products. Copper embossing is one of the leading crafts of Uzbek decorative and applied art. A critical and analytical assessment of innovative ideas, artistic and technological features, which arose as a result of the inter-regional synthesis of a local school, is presented. The article includes a detailed analysis of exhibits in museum collections, a research of the scientific papers of leading art critics and differs from previous studies in this area.

Keywords: applied art, copper embossing, metalwork, ewer, tray, Bukhara school

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Authors: Ogar Ofut Tumenayu, Olga Shabalina

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This paper describes the application of two types of pedagogical agents’ technology with different functions in an adaptive educational game with the sole aim of improving learning and enhancing interactivities in Digital Educational Games (DEG). This idea could promote the elimination of some problems of DEG, like isolation in game-based learning, by introducing a tutor and tutee pedagogical agents. We present an analysis of a learning companion interacting in a peer tutoring environment as a step toward improving social interactions in the educational game environment. We show that tutor and tutee agents use different interventions and interactive approaches: the tutor agent is engaged in tracking the learner’s activities and inferring the learning state, while the tutee agent initiates interactions with the learner at the appropriate times and in appropriate manners. In order to provide motivation to prevent mistakes and clarity a game task, the tutor agent uses the help dialog tool to provide assistance, while the tutee agent provides collaboration assistance by using the hind tool. We presented our idea on a prototype game called “Pyramid Programming Game,” a 2D game that was developed using Libgdx. The game's Pyramid component symbolizes a programming task that is presented to the player in the form of a puzzle. During gameplay, the Agents can instruct, direct, inspire, and communicate emotions. They can also rapidly alter the instructional pattern in response to the learner's performance and knowledge. The pyramid must be effectively destroyed in order to win the game. The game also teaches and illustrates the advantages of utilizing educational agents such as TrA and TeA to assist and motivate students. Our findings support the idea that the functionality of a pedagogical agent should be dualized into an instructional and learner’s companion agent in order to enhance interactivity in a game-based environment.

Keywords: tutor agent, tutee agent, learner’s companion interaction, agent collaboration

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Authors: Tomas Trainys, Algimantas Venckauskas

Abstract:

Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Rajkumar Kolangarakandy

Abstract:

Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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