Search results for: lean literature classification

Authors: Martin Camps

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The two-thousand-mile border that divides the United States and Mexico is a “contact zone” of cultural friction and unbalanced power relations as defined by Mary Louise Pratt. The interest of this paper is to analyze digital platforms created to address the study and comprehension of the borderlands with pedagogical and research reasons. The paper explores ways to engage students in archival and analytical practices to build a repository of resources, links, and digital tools and consider how to adapt them to the study of the borderlands. Sites such as “Torn Apart / Separados,” “Digital Borderlands,” “Borderlands Archives Cartography,” and “Juaritos Literario” show visualizations, mapping, and access to materials and marginal literature on the border phenomenon. Analyzing these projects contributes to highlighting digital projects and the study of the border and how to engage in activism via the study of literature and the representation of a human tragedy that underscores the divisions and biopolitics imposed on the Global South and imagine the digital border futures.

Keywords: borderlands, digital humanities, activism, border literature

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Akhila Sara Varughese

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In the postmodern society, visual narratives became an emerging genre in the field of literature. Graphic literature focuses on the literal and symbolic layer of interpretation. The most salient feature of graphic literature is its exploration of the public history of events and life narratives. The Indian graphic literature re-interprets the canon, style and the form of texts in Indian Writing in English and it demands a new literacy and the structure of the English literature. With the help of visual-verbal language, the graphic narratives discuss various facets of contemporary India. Graphic novels have firmly identified itself with the art of storytelling because of its capability of expressing human experiences to the most. In the textual novels, the author usually deserts the imagination of the readers, but in the case of graphic narratives, due to the presence of visual elements, the interpretation becomes simpler. India is the second most populous country in the world with a long tradition of history and culture. Indian literature always tries to reconstruct Indian history in various modes of representation. The present paper focuses on the fictional articulation of Indian history through the graphic narratives and analyses how some historical events in India portrays. The paper also traces the differences in rendering the history in graphic novels with that of textual novels. The paper discusses how much the blending of words and images helps in represent the Indian history by analyzing the graphic novels like Kashmir Pending by Naseer Ahmed, Delhi Calm by Vishwajyoti Ghosh and Munnu by Malik Sajad.

Keywords: graphic novels, Indian history, representation, visual-verbal literacy

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of Artificial Intelligence (AI), specifically Deep Learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our pioneering approach introduces a hybrid model, amalgamating the strengths of two renowned Convolutional Neural Networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging

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Authors: Lae-Jeong Park, Kyusoo Chung, Jungho Moon

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This paper introduces an effective method of segmenting Korean text (place names in Korean) from a Korean road sign image. A Korean advanced directional road sign is composed of several types of visual information such as arrows, place names in Korean and English, and route numbers. Automatic classification of the visual information and extraction of Korean place names from the road sign images make it possible to avoid a lot of manual inputs to a database system for management of road signs nationwide. We propose a series of problem-specific heuristics that correctly segments Korean place names, which is the most crucial information, from the other information by leaving out non-text information effectively. The experimental results with a dataset of 368 road sign images show 96% of the detection rate per Korean place name and 84% per road sign image.

Keywords: segmentation, road signs, characters, classification

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Sandhya Rao Mehta

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As ChatGpt and other Artificial Intelligence (AI) forms are becoming part of our regular academic world, the consequences are being gradually discussed. The extent to which an essay written by a student is itself of any value if it has been downloaded by some form of AI is perhaps central to this discourse. A larger question is whether writing should be taught as an academic skill at all. In literature classrooms, this has major consequences as writing a traditional paper is still the single most preferred form of assessment. This study suggests that it is imperative to investigate alternative forms of assessment in literature, not only because the existing forms can be written by AI, but in a larger sense, students are increasingly skeptical of the purpose of such work. The extent to which an essay actually helps the students professionally is a question that academia has not yet answered. This paper suggests that using real-world tasks like creating podcasts, video tutorials, and websites is a far better way to evaluate students' critical thinking and application of ideas, as well as to develop digital skills which are important to their future careers. Using the example of a course in literature, this study will examine the possibilities and challenges of creating digital projects as a way of confronting the complexities of student evaluation in the future. The study is based on a specific university English as a Foreign Language (EFL) context.

Keywords: assessment, literature, digital humanities, chatgpt

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Authors: Jay Neil Garciso Verano, Peter Rosales Bobiles

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This study tried to integrate RA 9262 in literature through the use of film. It identified RA 9262 provisions reflected in the students’ concepts in their oral participation and written outputs and pointed out different attitudes toward violence against women and respect to women as shaped by the film through their responses. Four Literature 121 (World Literature) classes with more or less similar characteristics participated in this study. The discussion of Paulette Kelly’s I Got Flowers Today took place during the first session while the viewing of the film Enough and discussion of the film followed to enrich and bolster students’ concepts and awareness on violence against women and to introduce RA 9262 provisions. The students’ attitudes toward violence against women and respect to women were lifted from the students’ oral and written responses. The film Enough presented eight provisions from RA 9262 reflected in students’ concepts which centered on the acts of violence against women tarnishing women’s rights and dignity. There were 25 attitudes toward violence against women and respect to women which surfaced, 11 of which are what initiate the acts, seven tell about the results from or effects of violence against women, and another seven exemplify respect to women. With the findings, it can be viewed that RA 9262 can be integrated in a literature course to awaken students’ minds on the prevalent issues on violating women’s rights and dignity. The discussion of Paulette Kelly’s I Got Flowers Today reinforced by the viewing of Enough deduced issues on the violation of women’s rights and dignity, attitudes toward violence against women, and students’ perception with regard respect to women.

Keywords: anti-violence against women, literature, film, enough, feminism

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Authors: Sherrene Bogle, Verlia Bogle, Tyrone Anderson

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In recent years, organizations have become increasingly interested in the possibility of analyzing social media as a means of gaining meaningful feedback about their products and services. The aspect based sentiment analysis approach is used to predict the sentiment for Twitter datasets for Digicel and Lime, the main mobile companies in Jamaica, using supervised learning classification techniques. The results indicate an average of 82.2 percent accuracy in classifying tweets when comparing three separate classification algorithms against the purported baseline of 70 percent and an average root mean squared error of 0.31. These results indicate that the analysis of sentiment on social media in order to gain customer feedback can be a viable solution for mobile companies looking to improve business performance.

Keywords: machine learning, sentiment analysis, social media, supervised learning

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Authors: Anna Sanczyk

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Diverse students may experience different forms of discrimination. Some of the oppression students experience in schools are racism, sexism, classism, or homophobia that may affect their achievement, and teachers need to make sure they create inclusive, equitable classroom environments. The broader literature on social change in education shows that teachers who challenge oppression and want to promote equitable and transformative education face institutional, social, and political constraints. This paper discusses research on teachers’ work to create socially just and culturally inclusive classrooms and schools. The practical contribution of this literature review is that it provides a comprehensive compilation of the studies presenting teachers’ roles and efforts in affecting social change. The examination of the research on social change in education points to the urgency of teachers addressing the needs of marginalized students and resisting systemic oppression in schools. The implications of this literature review relate to the concerns that schools should provide greater advocacy for marginalized students in diverse learning contexts, and teacher education programs should prepare teachers to be active advocates for diverse students. The literature review has the potential to inform educators to enhance educational equity and improve the learning environment. This literature review illustrates teachers as agents of change in diverse classrooms and contributes to understanding various ways of taking action towards fostering more equitable and transformative education in today’s schools.

Keywords: agents of change, diversity, opression, social change

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Authors: Vesna Pungercar, Florian Musso

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Identifying materials that can substitute rare or expensive natural resources is one of the key challenges for improving resource efficiency in the building sector. With a growing world population and rising living standards, more and more salt is produced as waste through seawater desalination and potash mining processes. Unfortunately, most of the salt is directly disposed of into nature, where it causes environmental pollution. On the other hand, salt is affordable, is used therapeutically in various respiratory treatments, and can store humidity and heat. It was, therefore, necessary to determine salt materials already in use in building construction and their hygrothermal properties. This research aims to identify salt materials from different scientific branches and historically, to investigate their properties and prioritize the most promising salt materials for indoor applications in a thermal envelope. This was realized through literature review and classification of salt materials into three groups (raw salt materials, composite salt materials, and processed salt materials). The outcome of this research shows that salt has already been used as a building material for centuries and has a potential for future applications due to its hygrothermal properties in a thermal envelope.

Keywords: salt, building material, hygrothermal properties, environment

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Maximilian A. Richter

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The public authority needs to understand the role and impacts of autonomous vehicle (AV) on the mobility system. At present, however, research shows that the impact of AV on cities varies. As a consequence, it is difficult to make recommendations to policymakers on how they should prepare for the future when so much remains unknown about this technology. The study aims to provide an overview of the literature on how autonomous vehicles will affect the cities and traffic of the future. To this purpose, the most important studies are first selected, and their results summarized. Further on, it will be clarified which advantages AV have for cities and how it can lead to an improvement in the current problems/challenges of cities. To achieve the research aim and objectives, this paper approaches a literature review. For this purpose, in a first step, the most important studies are extracted. This is limited to studies that are peer-reviewed and have been published in high-ranked journals such as the Journal of Transportation: Part A. In step 2, the most important key performance indicator (KPIs) (such as traffic volume or energy consumption) are selected from the literature research. Due to the fact that different terms are used in the literature for similar statements/KPIs, these must first be clustered. Furthermore, for each cluster, the changes from the respective studies are compiled, as well as their survey methodology. In step 3, a sensitivity analysis per cluster is made. Here, it will be analyzed how the different studies come to their findings and on which assumptions, scenarios, and methods these calculations are based. From the results of the sensitivity analysis, the success factors for the implementation of autonomous vehicles are drawn, and statements are made under which conditions AVs can be successful.

Keywords: autonomous vehicles, city of the future, literature review, traffic simulations

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: Espen Gronlie

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This paper will suggest that Ezra Pound and James Joyce are paradigmatic for two different approaches to literature and visual arts. Both authors are infamous for being difficult, but this does not mean that their works are similar. Pound famously promoted Joyce’s Ulysses and was instrumental in getting the work published in literary reviews. However, Pound did not appreciate Joyce’s artistic development in his so-called Work in Progress, which was published in 1939 under the title Finnegans Wake. Pound and Joyce will be read as representing two different approaches to literature and other forms of art. Pound can be seen as essentially influenced by cubism and modernist techniques such as collage and montage. While many critics have used these notions to describe The Cantos, this paper will suggest reading Pound’s opus magnum in relation to Finnegans Wake. The latter work shows how Joyce remained tied to an idea of the literary work as sound, as something which may – or perhaps even should – be read aloud. In contrast, Pound’s The Cantos show clear signs of being influenced by experiments in the visual arts. The paper will argue that Pound intended to develop his work in order to bring literature 'up to date' with the development in visual arts, while Joyce stuck to a more classical understanding of the literary work as composed for oral presentation.

Keywords: collage, conceptualism, montage, literature and visual arts

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Authors: Erkingul Soltanaeva, Omyrkhan Abdimanuly, Alua Temirbolat

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In this article, the public and social functions of literature and art in the Republic of Kazakhstan were analyzed on the basis of formal and informal literary organizations. The external and internal, subjective and objective factors which influenced the modern literary process were determined. The literary forces, their consolidation, types of organization in the art of word were examined. The periods of the literary process as planning, organization, promotion, and evaluation and their leading forces and approaches were analyzed. The right point of view to the language and mentality of the society force will influence to the literary process. The Ministry of Culture, the Writers' Union of RK and various non-governmental organizations are having different events for the promotion of literary process and to glorify literary personalities in the entire territory of Kazakhstan. According to the cultural plan of different state administration, there was a big program in order to publish their literary encyclopedia, to glorify and distribute books of own poets and writers of their region to the country. All of these official measures will increase the reader's interest in the book and will also bring up people to the patriotic education and improve the status of the native language. The professional literary publications such as the newspaper ‘Kazakh literature’, magazine ‘Zhuldyz’, and journal ‘Zhalyn’ materials which were published in the periods 2013-2015 on the basis of statistical analysis of the Kazakh literature topical to the issues and the field of themes are identified and their level of connection with the public situations are defined. The creative freedom, relations between society and the individual, the state of the literature, the problems of advantages and disadvantages were taken into consideration in the same articles. The level of functions was determined through the public role of literature, social feature, personal peculiarities. Now the stages as the literature management planning, organization, motivation, as well as the evaluation are forming and developing in Kazakhstan. But we still need the development of literature management to satisfy the actual requirements of the today’s agenda.

Keywords: literature management, material, literary process, social functions

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Authors: Susan Burkett-McKee, Bruce Knight, Michelle Vanderburg

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The wellbeing and educational success of high ability students are interrelated concepts with each potentially hindering or enhancing the other. A student’s well-being and educational success are also influenced by intrapersonal and interpersonal factors. This presentation begins with an exploration of the literature pertinent to the wellbeing and educational success of this cohort before an ecological perspective is taken to discuss research into the impact of interactions within the school context. While the literature consistently states that interactions exchanged between high ability students and school community members impact the students’ wellbeing or educational success, no consensus has been reached about whether the impact is positive or negative. Findings from the review shared in this presentation inform an interpretative phenomenological study involving senior secondary students enrolled in inclusive Australian schools to highlight, from the students’ perspective, the ways school-based interactions impact their wellbeing or educational success.

Keywords: educational success, interactions, literature review, wellbeing

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Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

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Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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This study considers the selection of the most suitable in silico molecular descriptors that could be used for s-triazine pesticides characterization. Suitable descriptors among topological, geometrical and physicochemical are used for quantitative structure-retention relationships (QSRR) model establishment. Established models were obtained using linear regression (LR) and multiple linear regression (MLR) analysis. In this paper, MLR models were established avoiding multicollinearity among the selected molecular descriptors. Statistical quality of established models was evaluated by standard and cross-validation statistical parameters. For detection of similarity or dissimilarity among investigated s-triazine pesticides and their classification, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used and gave similar grouping. This study is financially supported by COST action TD1305.

Keywords: chemometrics, classification analysis, molecular descriptors, pesticides, regression analysis

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Authors: Ilma Robo, Saimir Heta, Rialda Xhizdari, Kers Kapaj

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Background: The ideal disinfectant for surfaces, instruments, air, skin, both in dentistry and in the fields of medicine, does not exist.This is for the sole reason that all the characteristics of the ideal disinfectant cannot be contained in one; these are the characteristics that if one of them is emphasized, it will conflict with the other. A disinfectant must be stable, not be affected by changes in the environmental conditions where it stands, which means that it should not be affected by an increase in temperature or an increase in the humidity of the environment. Both of these elements contradict the other element of the idea of an ideal disinfectant, as they disrupt the solubility ratios of the base substance of the disinfectant versus the diluent. Material and methods: The study aims to extract the constant of each disinfectant/antiseptic used during dental disinfection protocols, accompanied by the side effects of the surface of the skin or mucosa where it is applied in the role of antiseptic. In the end, attempts were made to draw conclusions about the best possible combination for disinfectants after a dental procedure, based on the data extracted from the basic literature required during the development of the pharmacology module, as a module in the formation of a dentist, against data published in the literature. Results: The sensitivity of the disinfectant to changes in the atmospheric conditions of the environment where it is kept is a known fact. The care against this element is always accompanied by the advice on the application of the specific disinfectant, in order to have the desired clinical result. The constants of disinfectants according to the classification based on the data collected and presented are for alcohols 70-120, glycols 0.2, aldehydes 30-200, phenols 15-60, acids 100, povidone iodine halogens 5-75, hypochlorous acid halogens 150, sodium hypochlorite halogens 30-35, oxidants 18-60, metals 0.2-10. The part of halogens should be singled out, where specific results were obtained according to the representatives of this class, since it is these representatives that find scope for clinical application in dentistry. Conclusions: The search for the "ideal", in the conditions where its defining criteria are also established, not only for disinfectants but also for any medication or pharmaceutical product, is an ongoing search, without any definitive results. In this mine of data in the published literature if there is something fixed, calculable, such as the specific constant for disinfectants, the search for the ideal is more concrete. During the disinfection protocols, different disinfectants are applied since the field of action is different, including water, air, aspiration devices, tools, disinfectants used in full accordance with the production indications.

Keywords: disinfectant, constant, ideal, side effects

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Authors: Firas Al-Jubouri

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Literature has been a subject of studying English at secondary, university, and postgraduate levels in many countries and for several decades. One of the prominent literary genres, which is being increasingly used in the literature classrooms, is dystopian literature. However, since teachers usually address the educational requirements of teaching canonical English literature to meet the expected objectives of the particular 1organisation, and the learner’s needs in the non- Anglophone context, they must also negotiate the issues of cultural differences, aesthetic values, literary significance, and the rationale of storytelling. This paper examines how teaching certain dystopian themes in Aldous Huxley’s Brave New World (1932), an extremely influential dystopian canon, has to take into consideration the ideas, traditions, cultures, and means of literary interpretation inherent in the Arab Muslim world, with specific emphasis on the GCC region. It suggests the use of DionýzĎurišin’s (1929-1997) system of interliterariness in teaching world and comparative literature to help improve the interpretation of canonical literary texts in the international and inter-ethnic classrooms and contexts. Thus, this study helps to define a means of integrating global content and cross-cultural experiences into an effective teaching methodology that helps mitigate the major divides between the Anglophone text and the non-Anglophone readers.

Keywords: anglophone, dystopia, brave new world, huxley, interliterariness

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Authors: M. Taha, Hala H. Zayed, T. Nazmy, M. Khalifa

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Recently, traffic monitoring has attracted the attention of computer vision researchers. Many algorithms have been developed to detect and track moving vehicles. In fact, vehicle tracking in daytime and in nighttime cannot be approached with the same techniques, due to the extreme different illumination conditions. Consequently, traffic-monitoring systems are in need of having a component to differentiate between daytime and nighttime scenes. In this paper, a HSV-based day/night detector is proposed for traffic monitoring scenes. The detector employs the hue-histogram and the value-histogram on the top half of the image frame. Experimental results show that the extraction of the brightness features along with the color features within the top region of the image is effective for classifying traffic scenes. In addition, the detector achieves high precision and recall rates along with it is feasible for real time applications.

Keywords: day/night detector, daytime/nighttime classification, image classification, vehicle tracking, traffic monitoring

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: fractional differential (FD), computed tomography (CT), fusion, aplha, texture features.

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

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In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

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Authors: Malin Isaksson

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Teaching French as a foreign language usually implies teaching French literature, especially in higher education. Training university students in literary reading in a foreign language requires addressing several aspects at the same time: the (foreign) language, the poetic language, the aesthetic aspects of the studied works, and various interpretations of them. A pilot study sought to test a teaching model that would support students in learning to perform competent readings and short analyses of French literary works, in a rather independent manner. This shared practice paper describes the use of a flipped classroom method in two French literature courses, a campus course and an online course, and suggests that the teaching model may provide efficient tools for teaching literary reading and analysis in a foreign language. The teaching model builds on a high level of student activity and focuses on attentive reading, meta-perspectives such as theoretical concepts, individual analyses by students where said concepts are applied, and group discussions of the studied texts and of possible interpretations.

Keywords: attentive reading, flipped classroom, literature in foreign language studies, teaching literature analysis

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Authors: Johanna Lundqvist

Abstract:

Third cycle supervision is a multifaceted and complex task for supervisors in higher education. It progresses over several years and is affected by several proximal and distal factors. It can result in positive learning outcomes for doctoral students and high-quality publications. However, not all doctoral students thrive during their doctoral studies; nor do they all complete their studies. This is problematic for both the individuals themselves as well as society at large: doctoral students are valuable and important in current research, future research and higher education. The aim of this literature review is to elucidate what responsible third cycle supervision can include and be in practice. The question posed is as follows: according to recent literature, what is it that characterises responsible third cycle supervision in which doctoral students can thrive and develop their research knowledge and skills? A literature review was conducted, and the data gathered from the literature regarding responsible third cycle supervision was analysed by means of a thematic analysis. The analysis was inspired by the notion of responsible inclusion outlined by David Mitchell. In this study, the term literature refers to research articles and regulations. The results (preliminary) show that responsible third cycle supervision is associated with a number of interplaying factors (themes). These are as follows: committed supervisors and doctoral students; a clear vision and research problem; an individual study plan; adequate resources; interaction processes and constructive feedback; creativity; cultural awareness; respect and research ethics; systematic quality work and improvement efforts; focus on overall third cycle learning goals; and focus on research presentations and publications. Thus, responsible third cycle supervision can occur if these factors are realized in practice. This literature review is of relevance to evaluators, researchers, and management in higher education, as well as third cycle supervisors.

Keywords: doctoral student, higher education, third cycle supervisors, third cycle programmes

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Authors: Asmaa M. Hussein, Amr Wassal, Ahmed Farouk Al-Sadek, A. F. Abd El-Rahman

Abstract:

In this study, a multivariate analysis of potato spectroscopic data was presented to detect the presence of brown rot disease or not. Near-Infrared (NIR) spectroscopy (1,350-2,500 nm) combined with multivariate analysis was used as a rapid, non-destructive technique for the detection of brown rot disease in potatoes. Spectral measurements were performed in 565 samples, which were chosen randomly at the infection place in the potato slice. In this study, 254 infected and 311 uninfected (brown rot-free) samples were analyzed using different advanced statistical analysis techniques. The discrimination performance of different multivariate analysis techniques, including classification, pre-processing, and dimension reduction, were compared. Applying a random forest algorithm classifier with different pre-processing techniques to raw spectra had the best performance as the total classification accuracy of 98.7% was achieved in discriminating infected potatoes from control.

Keywords: Brown rot disease, NIR spectroscopy, potato, random forest

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Authors: T. Garba, Y. Y. Babanyara, T. O. Quddus, A. K. Mukatari

Abstract:

Human activities make environmental parameters in order to keep on changing globally. While some changes are necessary and beneficial to flora and fauna, others have serious consequences threatening the survival of their natural habitat if these changes are not properly monitored and mitigated. In-situ assessments are characterized by many challenges due to the absence of time series data and sometimes areas to be observed or monitored are inaccessible. Satellites Remote Sensing provide us with the digital images of same geographic areas within a pre-defined interval. This makes it possible to monitor and detect changes of environmental phenomena. This paper, therefore, reviewed the commonly use changes detection techniques globally such as image differencing, image rationing, image regression, vegetation index difference, change vector analysis, principal components analysis, multidate classification, post-classification comparison, and visual interpretation. The paper concludes by suggesting the use of more than one technique.

Keywords: environmental phenomena, change detection, monitor, techniques

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Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

Abstract:

This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

Procedia PDF Downloads 323