Search results for: offline signature recognition

Authors: Rachael Collins, George Lafford

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We report a case of a generally fit and a well 54-year-old gentleman who presented with a two-day history of worsening left-sided otorrhea, headache, neck stiffness, vomiting and pyrexia on the background of a seven-week history of OE. His condition progressed dramatically as he developed symptoms consistent with acute complete cervical cord syndrome with radiological evidence of skull base osteomyelitis, parapharyngeal, retropharyngeal and paravertebral abscesses and sigmoid sinus thrombus. Ultimately he made a significant, although not complete, recovery. This case is unique in demonstrating how OE can develop into a potentially life-threatening condition. It emphasizes the importance of early diagnosis and treatment of OE, the recognition of ‘red flag’ symptoms and highlights the importance of a multi-disciplinary team (MDT) approach when managing complex complications of OE.

Keywords: ENT, neurology, otology, MDT

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Authors: Ioanna Taouki, Marie Lallier, David Soto

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Metacognition refers to the capacity to reflect upon our own cognitive processes. Although there is an ongoing discussion in the literature on the role of metacognition in learning and academic achievement, little is known about its neurodevelopmental trajectories in early childhood, when children begin to receive formal education in reading. Here, we evaluate the metacognitive ability, estimated under a recently developed Signal Detection Theory model, of a cohort of children aged between 6 and 7 (N=60), who performed three two-alternative-forced-choice tasks (two linguistic: lexical decision task, visual attention span task, and one non-linguistic: emotion recognition task) including trial-by-trial confidence judgements. Our study has three aims. First, we investigated how metacognitive ability (i.e., how confidence ratings track accuracy in the task) relates to performance in general standardized tasks related to students' reading and general cognitive abilities using Spearman's and Bayesian correlation analysis. Second, we assessed whether or not young children recruit common mechanisms supporting metacognition across the different task domains or whether there is evidence for domain-specific metacognition at this early stage of development. This was done by examining correlations in metacognitive measures across different task domains and evaluating cross-task covariance by applying a hierarchical Bayesian model. Third, using robust linear regression and Bayesian regression models, we assessed whether metacognitive ability in this early stage is related to the longitudinal learning of children in a linguistic and a non-linguistic task. Notably, we did not observe any association between students’ reading skills and metacognitive processing in this early stage of reading acquisition. Some evidence consistent with domain-general metacognition was found, with significant positive correlations between metacognitive efficiency between lexical and emotion recognition tasks and substantial covariance indicated by the Bayesian model. However, no reliable correlations were found between metacognitive performance in the visual attention span and the remaining tasks. Remarkably, metacognitive ability significantly predicted children's learning in linguistic and non-linguistic domains a year later. These results suggest that metacognitive skill may be dissociated to some extent from general (i.e., language and attention) abilities and further stress the importance of creating educational programs that foster students’ metacognitive ability as a tool for long term learning. More research is crucial to understand whether these programs can enhance metacognitive ability as a transferable skill across distinct domains or whether unique domains should be targeted separately.

Keywords: confidence ratings, development, metacognitive efficiency, reading acquisition

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Authors: Fatemeh Zeinali Sehrig

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This study aimed to evaluate the expression of miR-299-3p, DNMT1, DNMT3A, and DNMT3B in breast cancer samples and investigate their diagnostic significance. Using the GSE40525 and GSE45666, the miR-299-3p expression level was studied in breast cancer tissues. Also, the expression levels of DNMT1, DNMT3A, and DNMT3B were investigated by analyzing GSE61725, GSE86374, and GSE37751 datasets. The target genes were studied in terms of biological processes of molecular functions and cellular components. Consistent with the in silico results, miR-299-3p expression was substantially decreased in breast cancer tissues, and the expression levels of DNMT1, DNMT3A, and DNMT3B were considerably upregulated in breast cancer samples. It was found that the expression levels of miR-299-3p and DNMT1, DNMT3A, and DNMT3B could be valuable diagnostic tools for detecting breast cancer. Also, miR-299-3p downregulation may play a role in DNMT1, DNMT3A, and DNMT3B upregulation in breast cancer.

Keywords: breast cancer, miR-299-3p, DNMTs, GEO database

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

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Authors: Carmen Noheda

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This text provides a reflection on the way of thinking about the study of the history of music by examining the production of historiography in Spain at the turn of the century. Based on concepts developed by the historical theorist Jörn Rüsen, the article focuses on the following aspects: the theoretical artifacts that structure the interpretation of the limits of writing the history of music, the narrative patterns used to give meaning to the discourse of history, and the orientation context that functions as a source of criteria of significance for both interpretation and representation. This analysis intends to show that historical music theory is not only a means to abstractly explore the complex questions connected to the production of historical knowledge, but also a tool for obtaining concrete images about the intellectual practice of professional musicologists. Writing about the historiography of contemporary Spanish music is a task that requires both a knowledge of the history that is being written and investigated, as well as a familiarity with current theoretical trends and methodologies that allow for the recognition and definition of the different tendencies that have arisen in recent decades. With the objective of carrying out these premises, this project takes as its point of departure the 'immediate historiography' in relation to Spanish music at the beginning of the 21st century. The hesitation that Spanish musicology has shown in opening itself to new anthropological and sociological approaches, along with its rigidity in the face of the multiple shifts in dynamic forms of thinking about history, have produced a standstill whose consequences can be seen in the delayed reception of the historiographical revolutions that have emerged in the last century. Methodologically, this essay is underpinned by Rüsen’s notion of the disciplinary matrix, which is an important contribution to the understanding of historiography. Combined with his parallel conception of differing paradigms of historiography, it is useful for analyzing the present-day forms of thinking about the history of music. Following these theories, the article will in the first place address the characteristics and identification of present historiographical currents in Spanish musicology to thereby carry out an analysis based on the theories of Rüsen. Finally, it will establish some considerations for the future of musical historiography, whose atrophy has not only fostered the maintenance of an ingrained positivist tradition, but has also implied, in the case of Spain, an absence of methodological schools and an insufficient participation in international theoretical debates. An update of fundamental concepts has become necessary in order to understand that thinking historically about music demands that we remember that subjects are always linked by reciprocal interdependencies that structure and define what it is possible to create. In this sense, the fundamental aim of this research departs from the recognition that the history of music is embedded in the conditions that make it conceivable, communicable and comprehensible within a society.

Keywords: historiography, Jörn Rüssen, Spanish musicology, theory of history of music

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Authors: Katsumi Hirata

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To develop the useful acoustic environmental recognition system, the method of estimating 3D-position of a stationary random acoustic source using bispectral analysis of 4-point detected signals is proposed. The method uses information about amplitude attenuation and propagation delay extracted from amplitude ratios and angles of auto- and cross-bispectra of the detected signals. It is expected that using bispectral analysis affects less influence of Gaussian noises than using conventional power spectral one. In this paper, the basic principle of the method is mentioned first, and its validity and features are considered from results of the fundamental experiments assumed ideal circumstances.

Keywords: 4-point detection, a stationary random acoustic source, auto- and cross-bispectra, estimation of 3D-position

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Authors: Mark Omu

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Education has remained the greatest fulcrum on which the developmental aspirations of societies and the world over is Anchored. This has been the case from the antiquity. As a result of recognition of this fact, education occupies a crucial and centripetal position at different epochs of societal formation and transformation. This paper recognized the all-embracing role of education to society and it utilized the literary research and review of literature to espouse on the role of alternative sources of financing education. This position was borne out of the dwindling resources available to education. Especially to finance teaching, learning, research and retraining of staffers. This paper found among other things that alternative funding of education is possible and it can be achieved through selling of its research products like entrepreneurial skills, collaborative ventures in public private partnership, philanthropic of endowments, etc. These are capable of bridging the financial gap currently bedevilling the educational sectors.

Keywords: alternative sources, funding, tertiary, education, society, partnership, Nigeria

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Authors: Fernando Cabo Aseguinolaza, Víctor Bouzas Blanco, Alberto Martí Ezpeleta

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Santiago de Compostela became a stable object of literary representation during the period between 1840 and 1915, approximately. This study offers a partial cartographical look at this process, suggesting that a cultural space like Compostela’s becoming an object of literary representation paralleled the first stages of its becoming a tourist destination. We use maps as a method of analysis to show the interaction between a corpus of novels and the emerging tradition of tourist guides on Compostela during the selected period. Often, the novels constitute ways to present a city to the outside, marking it for the gaze of others, as guidebooks do. That leads us to examine the ways of constructing and rendering communicable the local in other contexts. For that matter, we should also acknowledge the fact that a good number of the narratives in the corpus evoke the representation of the city through the figure of one who comes from elsewhere: a traveler, a student or a professor. The guidebooks coincide in this with the emerging fiction, of which the mimesis of a city is a key characteristic. The local cannot define itself except through a process of symbolic negotiation, in which recognition and self-recognition play important roles. Cartography shows some of the forms that these processes of symbolic representation take through the treatment of space. The research uses GIS to find significant models of representation. We used the program ArcGIS for the mapping, defining the databases starting from an adapted version of the methodology applied by Barbara Piatti and Lorenz Hurni’s team at the University of Zurich. First, we designed maps that emphasize the peripheral position of Compostela from a historical and institutional perspective using elements found in the texts of our corpus (novels and tourist guides). Second, other maps delve into the parallels between recurring techniques in the fictional texts and characteristic devices of the guidebooks (sketching itineraries and the selection of zones and indexicalization), like a foreigner’s visit guided by someone who knows the city or the description of one’s first entrance into the city’s premises. Last, we offer a cartography that demonstrates the connection between the best known of the novels in our corpus (Alejandro Pérez Lugín’s 1915 novel La casa de la Troya) and the first attempt to create package tourist tours with Galicia as a destination, in a joint venture of Galician and British business owners, in the years immediately preceding the Great War. Literary cartography becomes a crucial instrument for digging deeply into the methods of cultural production of places. Through maps, the interaction between discursive forms seemingly so far removed from each other as novels and tourist guides becomes obvious and suggests the need to go deeper into a complex process through which a city like Compostela becomes visible on the contemporary cultural horizon.

Keywords: compostela, literary geography, literary cartography, tourism

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Authors: Ji Eun Shin, Dong Hoon Lim

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Data clustering is a common technique used in data analysis and is used in many applications, such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing. K-means clustering is a well-known clustering algorithm aiming to cluster a set of data points to a predefined number of clusters. In this paper, we implement K-means algorithm based on MapReduce framework with RHadoop to make the clustering method applicable to large scale data. RHadoop is a collection of R packages that allow users to manage and analyze data with Hadoop. The main idea is to introduce a combiner as a function of our map output to decrease the amount of data needed to be processed by reducers. The experimental results demonstrated that K-means algorithm using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also showed that our K-means algorithm using RHadoop with combiner was faster than regular algorithm without combiner as the size of data set increases.

Keywords: big data, combiner, K-means clustering, RHadoop

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

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Authors: Dr. Dionysis Panos Dpt. Communication, Internet Studies Cyprus University of Technology

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The widespread use of New Media and particularly Social Media, through fixed or mobile devices, has changed in a staggering way our perception about what is “intimate" and "safe" and what is not, in interpersonal communication and social relationships. The distribution of self and identity-related information in communication now evolves under new and different conditions and contexts. Consequently, this new framework forces us to rethink processes and mechanisms, such as what "exposure" means in interpersonal communication contexts, how the distinction between the "private" and the "public" nature of information is being negotiated online, how the "audiences" we interact with are understood and constructed. Drawing from an interdisciplinary perspective that combines sociology, communication psychology, media theory, new media and social networks research, as well as from the empirical findings of a longitudinal comparative research, this work proposes an integrative model for comprehending mechanisms of personal information management in interpersonal communication, which can be applied to both types of online (Computer-Mediated) and offline (Face-To-Face) communication. The presentation is based on conclusions drawn from a longitudinal qualitative research study with 458 new media users from 24 countries for almost over a decade. Some of these main conclusions include: (1) There is a clear and evidenced shift in users’ perception about the degree of "security" and "familiarity" of the Web, between the pre- and the post- Web 2.0 era. The role of Social Media in this shift was catalytic. (2) Basic Web 2.0 applications changed dramatically the nature of the Internet itself, transforming it from a place reserved for “elite users / technical knowledge keepers" into a place of "open sociability” for anyone. (3) Web 2.0 and Social Media brought about a significant change in the concept of “audience” we address in interpersonal communication. The previous "general and unknown audience" of personal home pages, converted into an "individual & personal" audience chosen by the user under various criteria. (4) The way we negotiate the nature of 'private' and 'public' of the Personal Information, has changed in a fundamental way. (5) The different features of the mediated environment of online communication and the critical changes occurred since the Web 2.0 advance, lead to the need of reconsideration and updating the theoretical models and analysis tools we use in our effort to comprehend the mechanisms of interpersonal communication and personal information management. Therefore, is proposed here a new model for understanding the way interpersonal communication evolves, based on a revision of social penetration theory.

Keywords: new media, interpersonal communication, social penetration theory, communication exposure, private information, public information

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Authors: Sylwia Krawiec, Joanna Cabaj, Karol Malecha

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Nowadays, progress in the field of the analytical methods is of great interest for reliable biological research and medical diagnostics. Classical techniques of chemical analysis, despite many advantages, do not permit to obtain immediate results or automatization of measurements. Chemical sensors have displaced the conventional analytical methods - sensors combine precision, sensitivity, fast response and the possibility of continuous-monitoring. Biosensor is a chemical sensor, which except of conventer also possess a biologically active material, which is the basis for the detection of specific chemicals in the sample. Each biosensor device mainly consists of two elements: a sensitive element, where is recognition of receptor-analyte, and a transducer element which receives the signal and converts it into a measurable signal. Through these two elements biosensors can be divided in two categories: due to the recognition element (e.g immunosensor) and due to the transducer (e.g optical sensor). Working of optical sensor is based on measurements of quantitative changes of parameters characterizing light radiation. The most often analyzed parameters include: amplitude (intensity), frequency or polarization. Changes in the optical properties one of the compound which reacts with biological material coated on the sensor is analyzed by a direct method, in an indirect method indicators are used, which changes the optical properties due to the transformation of the testing species. The most commonly used dyes in this method are: small molecules with an aromatic ring, like rhodamine, fluorescent proteins, for example green fluorescent protein (GFP), or nanoparticles such as quantum dots (QDs). Quantum dots have, in comparison with organic dyes, much better photoluminescent properties, better bioavailability and chemical inertness. These are semiconductor nanocrystals size of 2-10 nm. This very limited number of atoms and the ‘nano’-size gives QDs these highly fluorescent properties. Rapid and sensitive detection of dopamine is extremely important in modern medicine. Dopamine is very important neurotransmitter, which mainly occurs in the brain and central nervous system of mammals. Dopamine is responsible for the transmission information of moving through the nervous system and plays an important role in processes of learning or memory. Detection of dopamine is significant for diseases associated with the central nervous system such as Parkinson or schizophrenia. In developed optical biosensor for detection of dopamine, are used graphene quantum dots (GQDs). In such sensor dopamine molecules coats the GQD surface - in result occurs quenching of fluorescence due to Resonance Energy Transfer (FRET). Changes in fluorescence correspond to specific concentrations of the neurotransmitter in tested sample, so it is possible to accurately determine the concentration of dopamine in the sample.

Keywords: biosensor, dopamine, fluorescence, quantum dots

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Authors: Leila Safazadeh, Brad Berron

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Self-assembled monolayers have tremendous impact in interfacial science, due to the unique opportunity they offer to tailor surface properties. Low-density self-assembled monolayers are an emerging class of monolayers where the environment-interfacing portion of the adsorbate has a greater level of conformational freedom when compared to traditional monolayer chemistries. This greater range of motion and increased spacing between surface-bound molecules offers new opportunities in tailoring adsorption phenomena in sensing systems. In particular, we expect low-density surfaces to offer a unique opportunity to intercalate surface bound ligands into the secondary structure of protiens and other macromolecules. Additionally, as many conventional sensing surfaces are built upon gold surfaces (SPR or QCM), these surfaces must be compatible with gold substrates. Here, we present the first stable method of generating low-density self assembled monolayer surfaces on gold for the analysis of their interactions with protein targets. Our approach is based on the 2:1 addition of thiol-yne chemistry to develop new classes of y-shaped adsorbates on gold, where the environment-interfacing group is spaced laterally from neighboring chemical groups. This technique involves an initial deposition of a crystalline monolayer of 1,10 decanedithiol on the gold substrate, followed by grafting of a low-packed monolayer on through a photoinitiated thiol-yne reaction in presence of light. Orthogonality of the thiol-yne chemistry (commonly referred to as a click chemistry) allows for preparation of low-density monolayers with variety of functional groups. To date, carboxyl, amine, alcohol, and alkyl terminated monolayers have been prepared using this core technology. Results from surface characterization techniques such as FTIR, contact angle goniometry and electrochemical impedance spectroscopy confirm the proposed low chain-chain interactions of the environment interfacing groups. Reductive desorption measurements suggest a higher stability for the click-LDMs compared to traditional SAMs, along with the equivalent packing density at the substrate interface, which confirms the proposed stability of the monolayer-gold interface. In addition, contact angle measurements change in the presence of an applied potential, supporting our description of a surface structure which allows the alkyl chains to freely orient themselves in response to different environments. We are studying the differences in protein adsorption phenomena between well packed and our loosely packed surfaces, and we expect this data will be ready to present at the GRC meeting. This work aims to contribute biotechnology science in the following manner: Molecularly imprinted polymers are a promising recognition mode with several advantages over natural antibodies in the recognition of small molecules. However, because of their bulk polymer structure, they are poorly suited for the rapid diffusion desired for recognition of proteins and other macromolecules. Molecularly imprinted monolayers are an emerging class of materials where the surface is imprinted, and there is not a bulk material to impede mass transfer. Further, the short distance between the binding site and the signal transduction material improves many modes of detection. My dissertation project is to develop a new chemistry for protein-imprinted self-assembled monolayers on gold, for incorporation into SPR sensors. Our unique contribution is the spatial imprinting of not only physical cues (seen in current imprinted monolayer techniques), but to also incorporate complementary chemical cues. This is accomplished through a photo-click grafting of preassembled ligands around a protein template. This conference is important for my development as a graduate student to broaden my appreciation of the sensor development beyond surface chemistry.

Keywords: low-density self-assembled monolayers, thiol-yne click reaction, molecular imprinting

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Authors: Marek Dosbaba

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Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux

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In France, the railway network is one of the main transport infrastructures and is the second largest European network. Therefore, railway inspection is an important task in railway maintenance to ensure safety for passengers using significant means in personal and technical facilities. Artificial vision has recently been applied to several railway applications due to its potential to improve the efficiency and accuracy when analyzing large databases of acquired images. In this paper, we present a vision system able to detect fastening elements based on artificial vision approach. This system acquires railway images using a CCD camera installed under a control carriage. These images are stitched together before having processed. Experimental results are presented to show that the proposed method is robust for detection fasteners in a complex environment.

Keywords: computer vision, image processing, railway inspection, image stitching, fastener recognition, neural network

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Authors: Yaron Covo, Tamar Kofman, Shira Palti

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Over the recent decades, debates about the engagement of fans in football governance have focused on the club level and national level, emphasizing the significance of fans’ involvement in increasing the connection of clubs with the community, and in safeguarding the transparency, accountability, and clubs’ financial stability. This paper will offer a different conceptual justification for providing fans with access to decision-making processes in football. First, it will suggest that the participation of fans is necessary for addressing discriminatory practices against women in football stadiums. Second, it will argue that fans’ involvement in football governance is important not only at the club and national level but also at the global level, relying on the principles of Global Administrative Law. In contemporary men’s football, female fans face different forms of discrimination. Iranian women are still prohibited from attending football games at the domestic level; In Saudi Arabia, female fans are only permitted to enter designated family areas; Qatar – the host of the 2022 FIFA world cup – requires women to attend matches wearing modest clothing. Similarly, in Turkey, Lebanon, UAE, and Algeria, women face cultural barriers when attending men’s football games. In other countries, female fans suffer from subtle discrimination, including micro-aggressions, misogyny, sexism, and noninstitutionalized exclusion. Despite the vital role of fans in world football and the importance of football for many women’s lives, little has been done to address this problem. While FIFA recognizes that these discriminatory practices contradict its statutes, this recognition fails to materialize into meaningful change. This paper will argue that FIFA’s omission stems from two interrelated characteristics of world football: (1) the ultra-masculine nature of the game; (2) the insufficient recognition of fans’ significance. While fans have been given a voice in various football bodies on the domestic level, FIFA has yet to allow the representation of fans as stakeholders in world football governance. Since fans are a more heterogeneous group than players, the voices of those fans who do not fit the ultra-masculine model are not heard. Thus, by focusing mainly on male players, FIFA reproduces the hegemonic masculinity that feeds back into fan dynamics and marginalizes female fans. To rectify this problem, we will call on FIFA to provide fans and female fans in particular, with voice mechanisms and access to decision-making processes. In addition to its impact on the formation of fans’ identities, such a move will allow fans to demand better enforcement of existing anti-discrimination norms and new regulations to address their needs. The literature has yet to address the relationship between fans’ gender discrimination and global football governance. Building on Global Administrative Law scholarship and feminist theories, this paper will aim to fill this gap.

Keywords: fans, FIFA, football governance, gender discrimination, global administrative law, human rights

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Authors: Zeinab Farhat, Nicolas Errien, Romuald Wernert, Véronique Verriele, Frédéric Amiard, Philippe Daniel

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Ovarian cancer, also known as the silent killer, is the fifth most common cancer among women worldwide, and its death rate is higher than that of other gynecological cancers. The low survival rate of women with high-grade serous ovarian carcinoma highlights the critical need for the development of new methods for early detection and diagnosis of the disease. The aim of this study was to evaluate if Raman spectroscopy combined with chemometric methods such as Principal Component Analysis (PCA) could differentiate between cancerous and normal tissues from different types of samples, such as paraffin embedding, chemical deparaffinized, formalin-fixed and fresh samples of the same normal and malignant ovarian tissue. The method was applied specifically to two critical spectral regions: the signature region (860-1000 〖cm〗^(-1)) and the high-frequency region (2800-3100 〖cm〗^(-1) ). The mean spectra of paraffin-embedded in normal and malignant tissues showed almost similar intensity. On the other hand, the mean spectra of normal and cancer tissues from chemical deparaffinized, formalin-fixed, and fresh samples show significant intensity differences. These spectral differences reflect variations in the molecular composition of the tissues, particularly lipids and proteins. PCA, which was applied to distinguish between cancer and normal tissues, was performed on whole spectra and on selected regions—the PCA score plot of paraffin-embedded shows considerable overlap between the two groups. However, the PCA score of chemicals deparaffinized, formalin-fixed, and fresh samples showed a good discrimination of tissue types. Our findings were validated by analyses of a set of samples whose status (normal and cancerous) was not previously known. The results of this study suggest that Raman Spectroscopy associated with PCA methods has the capacity to provide clinically significant differentiation between normal and cancerous ovarian tissues.

Keywords: Raman spectroscopy, ovarian cancer, signal processing, Principal Component Analysis, classification

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Reut Lanyado, David Mendlovic

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Most biological methods for authentication require special equipment and, some of them are easy to fake. We proposed a method for authentication based on hand movement while typing a sentence with a regular camera. This technique uses the full video of the hand, which is harder to fake. In the first phase, we tracked the hand joints in each frame. Next, we represented a single frame for each individual using our Pose Agnostic Rotation and Movement (PARM) dimensional space. Then, we indicated a full video of hand movement in a fixed low dimensional space using this method: Fixed Dimension Video by Interpolation Statistics (FDVIS). Finally, we identified each individual in the FDVIS representation using unsupervised clustering and supervised methods. Accuracy exceeds 96% for 80 individuals by using supervised KNN.

Keywords: authentication, feature extraction, hand recognition, security, signal processing

Procedia PDF Downloads 126

Authors: Hasna Elalaoui Elabdallaoui, Abdelaziz Elfazziki, Mohamed Sadgal

Abstract:

Outlines: The social economy is a moral approach to solidarity applied to the projects’ development. To reconcile economic activity and social equity, crowdfunding is as an alternative means of financing social projects. Several collaborative blockchain platforms exist. It eliminates the need for a central authority or an inconsiderate middleman. Also, the costs for a successful crowdfunding campaign are reduced, since there is no commission to be paid to the intermediary. It improves the transparency of record keeping and delegates authority to authorities who may be prone to corruption. Objectives: The objectives are: to define a software infrastructure for projects’ participatory financing within a social and solidarity economy, allowing transparent, secure, and fair management and to have a financial mechanism that improves financial inclusion. Methodology: The proposed methodology is: crowdfunding platforms literature review, financing mechanisms literature review, requirements analysis and project definition, a business plan, Platform development process and implementation technology, and testing an MVP. Contributions: The solution consists of proposing a new approach to crowdfunding based on Islamic financing, which is the principle of Mousharaka inspired by Islamic financing, which presents a financial innovation that integrates ethics and the social dimension into contemporary banking practices. Conclusion: Crowdfunding platforms need to secure projects and allow only quality projects but also offer a wide range of options to funders. Thus, a framework based on blockchain technology and Islamic financing is proposed to manage this arbitration between quality and quantity of options. The proposed financing system, "Musharaka", is a mode of financing that prohibits interests and uncertainties. The implementation is offered on the secure Ethereum platform as investors sign and initiate transactions for contributions using their digital signature wallet managed by a cryptography algorithm and smart contracts. Our proposal is illustrated by a crop irrigation project in the Marrakech region.

Keywords: social economy, Musharaka, blockchain, smart contract, crowdfunding

Procedia PDF Downloads 77

Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

Procedia PDF Downloads 136

Authors: Muhammad Ammar, Talha Ali, Abdul Basit, Bakhtawar Rajput, Zobia Sohail

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Music note detection is an area of study for the past few years and has its own influence in music file generation from sheet music. We proposed a method to detect music notes on sheet music using basic thresholding and blob detection. Subsequently, we created a notes dictionary using a semi-supervised learning approach. After notes detection, for each test image, the new symbols are added to the dictionary. This makes the notes detection semi-automatic. The experiments are done on images from a dataset and also on the captured images. The developed approach showed almost 100% accuracy on the dataset images, whereas varying results have been seen on captured images.

Keywords: music note, sheet music, optical music recognition, blob detection, thresholding, dictionary generation

Procedia PDF Downloads 181

Authors: Nateepat Srivarom, Weng Jingnong, Serm Chinnarat

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Precise Point Positioning (PPP) technique is use to improve accuracy by using precise satellite orbit and clock correction data, but this technique is complicated methods and high costs. Currently, there are several online processing service providers which offer simplified calculation. In the first part of this research, we compare the efficiency and precision of four software. There are three popular online processing service providers: Australian Online GPS Processing Service (AUSPOS), CSRS-Precise Point Positioning and CenterPoint RTX post processing by Trimble and 1 offline software, RTKLIB, which collected data from 10 the International GNSS Service (IGS) stations for 10 days. The results indicated that AUSPOS has the least distance root mean square (DRMS) value of 0.0029 which is good enough to be calculated for monitoring the movement of tectonic plates. The second, we use AUSPOS to process the data of geodetic network of Thailand. In December 26, 2004, the earthquake occurred a 9.3 MW at the north of Sumatra that highly affected all nearby countries, including Thailand. Earthquake effects have led to errors of the coordinate system of Thailand. The Royal Thai Survey Department (RTSD) is primarily responsible for monitoring of the crustal movement of the country. The difference of the geodetic network movement is not the same network and relatively large. This result is needed for survey to continue to improve GPS coordinates system in every year. Therefore, in this research we chose the AUSPOS to calculate the magnitude and direction of movement, to improve coordinates adjustment of the geodetic network consisting of 19 pins in Thailand during October 2013 to November 2017. Finally, results are displayed on the simulation map by using the ArcMap program with the Inverse Distance Weighting (IDW) method. The pin with the maximum movement is pin no. 3239 (Tak) in the northern part of Thailand. This pin moved in the south-western direction to 11.04 cm. Meanwhile, the directional movement of the other pins in the south gradually changed from south-west to south-east, i.e., in the direction noticed before the earthquake. The magnitude of the movement is in the range of 4 - 7 cm, implying small impact of the earthquake. However, the GPS network should be continuously surveyed in order to secure accuracy of the geodetic network of Thailand.

Keywords: precise point positioning, online processing service, geodetic network, inverse distance weighting

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Authors: Fatten F. Elkomy

Abstract:

The Syrian refugees are the largest refugee population since World War II. Mostly, children, these individuals were exposed to intense traumatic events in their homeland, throughout their journey, and during settlement in foreign lands. Art is a universal language to express feelings and tough human experiences. It is also a medium for healing and promoting creativity and resilience. Literature review was conducted to examine the use of art to facilitate psychiatric interviews, diagnosis, and therapy with traumatized children. Results show a severe impact of childhood trauma on the increased risk for abuse, neglect, and psychiatric disorders. Clinicians must recognize, evaluated and provide help for these children. In conclusion, drawings are used to tell a story, reflect deep emotions, and create a meaningful self-recognition and determination. Participants will understand art therapy using the expressive therapies continuum framework to evaluate drawings and to promote healing for refugee children.

Keywords: art therapy, children drawings, Syrian refugees, trauma in childhood

Procedia PDF Downloads 165

Authors: Brendah Nagula, Omolo Fredrick Okalebo, Ronald Ssengendo, Ivan Bamweyana

Abstract:

The Kampala Physical Development Plan (KPDP) was developed in 2012 and projected both long term and short term developments within the City .The purpose of the plan was to not only shape the city into a spatially planned area but also to control the urban sprawl trends that had expanded with pronounced instances of informal settlements. This plan was approved by the National Physical Planning Board and a signature was appended by the Minister in 2013. Much as the KPDP plan has been implemented using different approaches such as detailed planning, development control, subdivision planning, carrying out construction inspections, greening and beautification, there is still limited knowledge on the level of conformance towards this plan. Therefore, it is yet to be determined whether it has been effective in shaping the City into an ideal spatially planned area. Attaining a clear picture of the level of conformance towards the KPDP 2012 through evaluation between the planned and the existing land use in Kampala City was performed. Methods such as Supervised Classification and Post Classification Change Detection were adopted to perform this evaluation. Scrutiny of findings revealed Central Division registered the lowest level of conformance to the planning standards specified in the KPDP 2012 followed by Nakawa, Rubaga, Kawempe, and Makindye. Furthermore, mixed-use development was identified as the land use with the highest level of non-conformity of 25.11% and institutional land use registered the highest level of conformance of 84.45 %. The results show that the aspect of location was not carefully considered while allocating uses in the KPDP whereby areas located near the Central Business District have higher land rents and hence require uses that ensure profit maximization. Also, the prominence of development towards mixed-use denotes an increased demand for land towards compact development that was not catered for in the plan. Therefore in order to transform Kampala city into a spatially planned area, there is need to carefully develop detailed plans especially for all the Central Division planning precincts indicating considerations for land use densification.

Keywords: spatial plan, post classification change detection, Kampala city, landuse

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Authors: Sara Namdarian, Hafez Salleh

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This paper scrutinizes the circumstances of the application of Augmented Reality (AR) technology to enhance the adaptability of architecture classrooms in private Malaysian university classrooms. This study aims to indicate the constraints of mono-functional classrooms in comparison to the potentials of multi-functional classrooms derived from AR application through an exploratory mixed method strategy. This paper expects to contribute towards recognition of suitable AR techniques which can be applied in the development of Adaptive-AR-Classroom-Systems (AARCS) in architecture classrooms. The findings, derived from the analysis, show current classrooms have limited functional spaces, and concludes that AR application can be used in design classrooms to provide a variety of visuals and virtual objects that are required in conducting architecture projects in higher educational centers.

Keywords: design activity, space enhancement, design education, architectural design augmented reality

Procedia PDF Downloads 446

Authors: B. G. Abiona, E. O. Fakoya, D. O. Alabi

Abstract:

Influence of leadership roles on agricultural employees’ job satisfaction was studied. Data were from 68 randomly selected respondents. Major leadership roles include supervision of employees work (x̄=3.67), leaders were goal oriented (x̄=3.39), dissemination of information among the employees (x̄=3.35). Major employees’ satisfaction was: Employees work together with their colleagues (x̄=3.54) and also interact freely with their colleagues (x̄=3.51). Major challenges affecting employees job satisfaction were inadequate funding (x̄=3.30), irregular leave bonus (x̄=3.29), climate and weather condition (x̄=3.08) and inadequate incentive (x̄=3.02). Regression analysis showed a positive significant coefficient (P<0.05) exist between religion (p<0.05), educational status(p<0.05), year of service(p<0.05), leadership roles (p<0.005), challenges faced by respondents(P<0.05), and employees’ job satisfaction. For adequate leadership role, organization should pay attention to disbursement of training funds, availability of adequate incentive and leadership recognition.

Keywords: leadership roles, agricultural employees’, job satisfaction, institute, Nigeria

Procedia PDF Downloads 297

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

Abstract:

Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

Procedia PDF Downloads 119