Search results for: fused deep representations

Authors: Mathias Ebot, Päivi Harinen

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This article focuses on challenges of parenthood for Sub-Saharan African fathers in Finland. In this analysis Finland represents a Nordic welfare society where family relationships are strongly guided by national family policies and discourses. These policies are based on both traditional ideas of a proper Finnish family, as well as on the contemporary waves of female liberation and emphasis on children’s rights. We analyze how especially Cameroonian fathers perceive their parenthood positions and how they characterize and frame their fathering experiences in relation to the mainstream sociopolitical, cultural and national representations of fatherhood in Finland. The analysis is based on interviews and narrative reports collected among Cameroonian fathers living in Finland with their African spouses. The scrutiny shows that in the context where the mainstream cultural and national family representation is created by equality between parents and also between parents and their children, and where “good fatherhood” is created by embodied presence and warm relationships with children these fathers have difficulties: They have to fulfill another fatherhood duty – bread-winning – and thus ensure their labor possibilities all the time, from very marginalized positions of the labor market. When comparing their fatherhood position with the one in Cameroon they also feel embarrassed as the Finnish educational system teaches and encourages their children to challenge their authority as up-raising adults, which in Cameroon could not be possible.

Keywords: Cameroonian fathers, perception, fathering experiences, Finland

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Authors: Arjun Chatterjee

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The India-China border clash in Galwan valley in June 2020, the first deadly skirmish between the two Asian giants in the Himalayan border area in over four decades, highlighted the need to examine the notion of ‘authenticity’ in journalistic practices. Information emanating from such remotely located, sparsely populated, and not well-demarcated international land borders have limited sources, restricted to official sources, which have their own narrative. Geopolitical goals and ambitions embolden narratives of nationalism in the media, and these often challenge the notion and understanding of authenticity in journalism. The Indian press, contrary to the Chinese press, which is state-owned, is diverse and also confrontational, where narratives of nationalism are differentially interpreted, embedded, and realised. This paper examines how authenticity has become a variable, rather than a constant, in conflict reporting of the Sino-Indian border clash and how authenticity is interpreted similarly or differently in conflict journalism. The paper reports qualitative textual analysis of two leading English language newspapers – The Times of India and The Hindu, and two mainstream regional language newspapers, Amar Ujala (Hindi) and Ananda Bazar Patrika (Bengali), to evaluate the ways in which representations of information function in conflict reporting and to recontextualize (and thus change or modify the meaning of) that which they represent, and with what political and cultural implications.

Keywords: India-China, framing, conflict, media narratives, border dispute

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Constanza Levicán, Andrés Aparicio, Rodrigo F. Cádiz

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The artistic usage of Brain-computer interfaces (BCI), initially intended for medical purposes, has increased in the past few years as they become more affordable and available for the general population. One interesting question that arises from this practice is whether it is possible to compose or perform music by using only the brain as a musical instrument. In order to approach this question, we propose a BCI for musical composition, based on the representation of some mental states as the musician thinks about sounds. We developed software, called Insight2OSC, that allows the usage of the Emotiv Insight device as a musical instrument, by sending the EEG data to audio processing software such as MaxMSP through the OSC protocol. We provide two compositional applications bundled with the software, which we call Mapping your Mental State and Thinking On. The signals produced by the brain have different frequencies (or rhythms) depending on the level of activity, and they are classified as one of the following waves: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-50 Hz). These rhythms have been found to be related to some recognizable mental states. For example, the delta rhythm is predominant in a deep sleep, while beta and gamma rhythms have higher amplitudes when the person is awake and very concentrated. Our first application (Mapping your Mental State) produces different sounds representing the mental state of the person: focused, active, relaxed or in a state similar to a deep sleep by the selection of the dominants rhythms provided by the EEG device. The second application relies on the physiology of the brain, which is divided into several lobes: frontal, temporal, parietal and occipital. The frontal lobe is related to abstract thinking and high-level functions, the parietal lobe conveys the stimulus of the body senses, the occipital lobe contains the primary visual cortex and processes visual stimulus, the temporal lobe processes auditory information and it is important for memory tasks. In consequence, our second application (Thinking On) processes the audio output depending on the users’ brain activity as it activates a specific area of the brain that can be measured using the Insight device.

Keywords: BCI, music composition, emotiv insight, OSC

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Onaza Ajmal

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The purpose of the study is to explore the power relations linguistically and visually with reference to the representation of gender, race, violence, and empowerment through male characters and female superheroes from the two selected Marvel comics, Ms. Marvel (2014) and Captain Marvel (2019-). The study also aims to elaborate on the different cultural backgrounds of female superheroes and their choices and behaviors concerning the male characters. Moreover, it also seeks to explore whether the female superheroes reassert or resists the established gender roles. Using the tenets of critical discourse analysis (CDA) and feminist critical discourse analysis (FCDA) by Lazar (2005), the study analyzed the power relations from a feminist viewpoint. The linguistic analysis of textual features such as ‘adjectives’, ‘lexical items’, ‘metaphors’, and ‘use of pronouns’, etc., found in the selected comics is carried out under the framework of CDA given by Fairclough (1989). Kress and van Leeuwen's model of reading images (2006) are used to analyze the visual images in this study. The findings of the study show that despite the empowering nature of female superheroes, the unequal power relations between male and female characters are established linguistically and visually, which further sustains and reinforces the racial and patriarchal gender ideologies in the selected comics. Moreover, it is recommended that the female representations in the feminist themes of empowerment with respect to the Pakistani female superheroes should also be explored for further research.

Keywords: feminist critical discourse analysis, patriarchal gender ideology, power relations, superhero comics

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Authors: Anoop T. R., Otman Basir, Robert F. Hess, Eileen E. Birch, Brooke A. Koritala, Reed M. Jost, Becky Luu, David Stager, Ben Thompson

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Strabismus refers to a misalignment of the eyes. Early detection and treatment of strabismus in childhood can prevent the development of permanent vision loss due to abnormal development of visual brain areas. We developed a two-stage method for strabismus detection and classification based on photographs of the face. The first stage detects the presence or absence of strabismus, and the second stage classifies the type of strabismus. The first stage comprises face detection using Haar cascade, facial landmark estimation, face alignment, aligned face landmark detection, segmentation of the eye region, and detection of strabismus using VGG 16 convolution neural networks. Face alignment transforms the face to a canonical pose to ensure consistency in subsequent analysis. Using facial landmarks, the eye region is segmented from the aligned face and fed into a VGG 16 CNN model, which has been trained to classify strabismus. The CNN determines whether strabismus is present and classifies the type of strabismus (exotropia, esotropia, and vertical deviation). If stage 1 detects strabismus, the eye region image is fed into stage 2, which starts with the estimation of pupil center coordinates using mask R-CNN deep neural networks. Then, the distance between the pupil coordinates and eye landmarks is calculated along with the angle that the pupil coordinates make with the horizontal and vertical axis. The distance and angle information is used to characterize the degree and direction of the strabismic eye misalignment. This model was tested on 100 clinically labeled images of children with (n = 50) and without (n = 50) strabismus. The True Positive Rate (TPR) and False Positive Rate (FPR) of the first stage were 94% and 6% respectively. The classification stage has produced a TPR of 94.73%, 94.44%, and 100% for esotropia, exotropia, and vertical deviations, respectively. This method also had an FPR of 5.26%, 5.55%, and 0% for esotropia, exotropia, and vertical deviation, respectively. The addition of one more feature related to the location of corneal light reflections may reduce the FPR, which was primarily due to children with pseudo-strabismus (the appearance of strabismus due to a wide nasal bridge or skin folds on the nasal side of the eyes).

Keywords: strabismus, deep neural networks, face detection, facial landmarks, face alignment, segmentation, VGG 16, mask R-CNN, pupil coordinates, angle deviation, horizontal and vertical deviation

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: Alona Faktor

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In this work, we present a NN-based computational model that can perform attention shifts according to high-level instruction. The instruction specifies the type of attentional shift using explicit geometrical relation. The instruction also can be of cognitive nature, specifying more complex human-human interaction or human-object interaction, or object-object interaction. Applying this approach sequentially allows obtaining a structural description of an image. A novel data-set of interacting humans and objects is constructed using a computer graphics engine. Using this data, we perform systematic research of relational segmentation shifts.

Keywords: cognitive science, attentin, deep learning, generalization

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Authors: Pakpoom Hannapha

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This paper investigates the implications of social and political contexts in Thai Post’s news articles written by a columnist, Khon Plai Soy. Samples included twenty eight news articles published between 28th May 2015 and 28th June 2015 selected and analyzed according to Semiotics including implications, connotation, cultural politics, and Thai usage in newspaper articles. The data analysis can be divided into two parts; first, an analysis of signs/signifiers appearing in the articles and second, an analysis of the columnist’s purposes. This study demonstrated representations of signs in the selected articles that were categorized into four groups: events, actions, persons, and organizations. In this study, implications of the news articles were analyzed in two aspects according to Semiotics. It was found that the columnist mostly points out purposes for education, facts, and personal opinions in his works. Also, he offers some solutions to problems discussed in the articles. The writer often explicated knowledge and facts in accordance with either his personal opinions or problem-solutions. According to the research result, studying the implications of news articles in the Thai Post based on the Semiotic approach can help clarify and understand connotative meanings in terms of contents and the writer’s purposes. This paper can enhance readers’ understanding of Semiotic implications through signs and meanings in the texts and thus be used as a model to explore other political news articles

Keywords: semiotic analysis, Thai social contexts, Thai Post’s news, articles

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Authors: Karthika Chandran, Pulkit Prakash, Amitabh Das, Santhosh P. N.

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Rare earth orthoferrites (RFeO₃) exhibit interesting and useful magnetic properties like multiferroicity, magnetodielectric coupling, spin reorientation (SR) and exchange bias. B site doped RFeO₃ are attracting attention due to the complex and tuneable magnetic transitions. In this work, 45% Mn-doped HoFeO₃ polycrystalline sample (HoFe₀.₅₅Mn₀.₄₅O₃) was synthesized by a solid-state reaction method. The magnetic structure and transitions were studied by magnetization measurements and neutron powder diffraction methods. The neutron diffraction patterns were taken at 13 different temperatures from 7°K to 300°K (7°K and 25°K to 300°K in 25°K intervals). The Rietveld refinement was carried out by using a FULLPROF suite. The magnetic space groups and the irreducible representations were obtained by SARAh module. The room temperature neutron diffraction refinement results indicate that the sample crystallizes in an orthorhombic perovskite structure with Pnma space group with lattice parameters a = 5.6626(3) Ǻ, b = 7.5241(3) Ǻ and c = 5.2704(2) Ǻ. The temperature dependent magnetization (M-T) studies indicate the presence of two magnetic transitions in the system ( TN Fe/Mn~330°K and TSR Fe/Mn ~290°K). The inverse susceptibility vs. temperature curve shows a linear behavior above 330°K. The Curie-Weiss fit in this region gives negative Curie constant (-34.9°K) indicating the antiferromagnetic nature of the transition. The neutron diffraction refinement results indicate the presence of mixed magnetic phases Γ₄(AₓFᵧG

Keywords: neutron powder diffraction, rare earth orthoferrites, Rietveld analysis, spin reorientation

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Authors: Isahakyan Heghine

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Intertextuality, being in the centre of attention of both linguists and literary critics, is vividly expressed in the outstanding British novelist and philosopher J. Fowles' works. 'The Magus’ is a deep psychological and philosophical novel with vivid intertextual links with the Greek mythology and authors from different epochs. The aim of the paper is to show how intertextuality might serve as a dialogue between two authors (J. Fowles and J. Donne) disguised in the dialogue of two protagonists of the novel : Conchis and Nicholas. Contrastive viewpoints concerning man's isolation, loneliness are stated in the dialogue. Due to the conceptual analysis of the text it becomes possible both to decode the conceptual information of the text and find out its intertextual links.

Keywords: dialogue, conceptual analysis, isolation, intertextuality

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Authors: Rakesh Dogra, Arun Kumar, Arvind Kumar Sharma

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This paper reports the thermoluminescence as well as optical properties of rare earth doped lithium fluoride (LiF) nanophosphor, synthesized via chemical route. The rare earth impurities (Yb and Sm) have been observed to increase the deep trap center capacity, which, in turn, enhance the radiation resistance of the LiF. This suggests the viability of these materials to be used as high dose thermoluminescent detectors at high temperature. Further, optical absorption measurements revealed the formation of radiation induced stable color centers in LiF at room temperature, which are independent of the rare earth dopant.

Keywords: lithium flouride, thermoluminescence, UV-VIS spectroscopy, Gamma radiations

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Authors: Qianwei Cheng, A. K. M. Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, Moinul Zaber

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Thomas Friedman, in his famous book, argued that the world in this 21st century is flat and will continue to be flatter. This is attributed to rapid globalization and the interdependence of humanity that engendered tremendous in-flow of human migration towards the urban spaces. In order to keep the urban environment sustainable, policy makers need to plan based on extensive analysis of the urban environment. With the advent of high definition satellite images, high resolution data, computational methods such as deep neural network analysis, and hardware capable of high-speed analysis; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. However, the first step of understanding urban space lies in useful categorization of the space that is usable for data collection, analysis, and visualization. In this paper, we propose a pragmatic categorization method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. Categorization to plan sustainable urban spaces should encompass the buildings and their surroundings. However, the state-of-the-art is mostly dominated by classification of building structures, building types, etc. and largely represents the developed world. Hence, these methods and models are not sufficient for developing countries such as Bangladesh, where the surrounding environment is crucial for the categorization. Moreover, these categorizations propose small-scale classifications, which give limited information, have poor scalability and are slow to compute in real time. Our proposed method is divided into two steps-categorization and automation. We categorize the urban area in terms of informal and formal spaces and take the surrounding environment into account. 50 km × 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert and consequently a map was drawn. The categorization is based broadly on two dimensions-the state of urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four categories: 1) highly informal area; 2) moderately informal area; 3) moderately formal area; and 4) highly formal area. In total, sixteen sub-categories were identified. For semantic segmentation and automatic categorization, Google’s DeeplabV3+ model was used. The model uses Atrous convolution operation to analyze different layers of texture and shape. This allows us to enlarge the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used to train the model, and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean Intersection over Union (mIoU). In this paper, we propose a pragmatic categorization method that is readily applicable for automatic use in both developing and developed world context. The method can be augmented for real-time socio-economic comparative analysis among cities. It can be an essential tool for the policy makers to plan future sustainable urban spaces.

Keywords: semantic segmentation, urban environment, deep learning, urban building, classification

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Authors: Jocelyn Lachance, Francis Jauréguiberry

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In a few years, the exceptionality of the situation of an individual who could be reached at any time and at any time was replaced by the normality of instantly hearing the voice or immediately seeing the face of the person. This participates in the transformation of our representations of time and space, which gives rise to new expectations. Expectations that parents formulate more or less clearly to their children. The obligation to remain reachable seems to be asserting itself as a general norm which, having imposed itself on adults, now extends to the youngest. In the case of parents and their children, the rationale for this ongoing connection is not always based on actual and imminent dangers. It is the potential for dangerous events that underpins the indisputable argument for the importance of remaining reachable. It is the contingent nature of the risks that imposes itself on these young people as an argument of authority. By entering this connected world, the younger generations also end up adhering in many cases to this reassuring standard of connection. Many teenagers in ours researches nonetheless firmly believe that their freedom of movement is subject to the obligation to carry their smartphone with them. In this way, a connection "pact" is generally established, concluded under pressure, which implies first and foremost that contact be possible at any time, hence the importance of keeping it within reach, and often of '' be attentive to calls and texts sent by parents, at the risk of losing a recently acquired freedom. In this context, if adolescents are growing up in a connected world today, it is also because of the connection the parents are expecting from them. In our conference, by evoking situations reported by teenagers and parents of teenagers during our surveys, we propose to think about the role of the parents in making their child connected and about the desire of the disconnection of the teens.

Keywords: connection, disconnection, smartphone, parents, ritual

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Mitat Uysal, Aynur Uysal

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Computer engineering and mathematics share a deep and symbiotic relationship, with mathematics providing the foundational theories and models for computer engineering advancements. From algorithm development to optimization techniques, mathematics plays a pivotal role in solving complex computational problems. This paper explores key mathematical principles that underpin computer engineering, illustrating their significance through a case study that demonstrates the application of optimization techniques using Python code. The case study addresses the well-known vehicle routing problem (VRP), an extension of the traveling salesman problem (TSP), and solves it using a genetic algorithm.

Keywords: VRP, TSP, genetic algorithm, computer engineering, optimization

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Authors: Asmaa Zaki M., Ahmed Abd El Aziz, Heba A. Fayed, Moustafa H. Aly

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ISLs are the most popular choice for deep space communications because these links are attractive alternatives to present day microwave links. This paper explored the allowable high data rate in this link over different orbits, which is affected by variation in modulation scheme and detector type. Moreover, the objective of this paper is to optimize and analyze the performance of ISL in terms of Q-factor and Minimum Bit Error Rate (Min-BER) based on different detectors comprising some parameters.

Keywords: free space optics (FSO), field of view (FOV), inter satellite link (ISL), optical wireless communication (OWC)

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Michel Moliere

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In the next decades, the thermal power generation segment will survive only if it achieves deep mutations, including drastical abatements of CO2 emissions and strong efficiency gains. In this challenging perspective, stationary gas turbines appear as serious candidates to lead the energy transition. Indeed, during the past decades, these turbomachines have made brisk technological advances in terms of efficiency, reliability, fuel flex (including the combustion of hydrogen), and the ability to hybridize with regenrables. It is, therefore, timely to summarize the progresses achieved by gas turbines in the recent past and to examine what are their assets to face the challenges of the energy transition.

Keywords: energy transition, gas turbines, decarbonization, power generation

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Authors: Chen Liu, Dawit Negussey

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EPS (Expanded Polystyrene) geofoam as light-weight material in geotechnical applications are made of pre-expanded resin beads that form fused cellular micro-structures. The strength and deformation properties of geofoam blocks are determined by unconfined compression of small test samples between rigid loading plates. Applied loads are presumed to be supported uniformly over the entire mating end areas. Predictions of field performance on the basis of such laboratory tests widely over-estimate actual post-construction settlements and exaggerate predictions of long-term creep deformations. This investigation examined the development of contact pressures at a large number of discrete points at low and large strain levels for different densities of geofoam. Development of pressure patterns for fine and coarse interface material textures as well as for molding skin and hot wire cut geofoam surfaces were examined. The lab testing showed that I-Scan tactile sensors are useful for detailed observation of contact pressures at a large number of discrete points simultaneously. At low strain level (1%), the lower density EPS block presents low variations in localized stress distribution compared to higher density EPS. At high strain level (10%), the dense geofoam reached the sensor cut-off limit. The imprint and pressure patterns for different interface textures can be distinguished with tactile sensing. The pressure sensing system can be used in many fields with real-time pressure detection. The research findings provide a better understanding of EPS geofoam behavior for improvement of design methods and performance prediction of critical infrastructures, which will be anticipated to guide future improvements in design and rapid construction of critical transportation infrastructures with geofoam in geotechnical applications.

Keywords: geofoam, pressure distribution, tactile pressure sensors, interface

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Authors: Dumont Maxime, Correia Carlos, Sauvage Jean-François, Schwartz Noah, Gray Morgan

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Context: The concept of the segmented telescope is unavoidable to build extremely large telescopes (ELT) in the quest for spatial resolution, but it also allows one to fit a large telescope within a reduced volume of space (JWST) or into an even smaller volume (Standard Cubesat). Cubesats have tight constraints on the computational burden available and the small payload volume allowed. At the same time, they undergo thermal gradients leading to large and evolving optical aberrations. The pupil segmentation comes nevertheless with an obvious difficulty: to co-phase the different segments. The CubeSat constraints prevent the use of a dedicated wavefront sensor (WFS), making the focal-plane images acquired by the science detector the most practical alternative. Yet, one of the challenges for the wavefront sensing is the non-linearity between the image intensity and the phase aberrations. Plus, for Earth observation, the object is unknown and unrepeatable. Recently, several studies have suggested Neural Networks (NN) for wavefront sensing; especially convolutional NN, which are well known for being non-linear and image-friendly problem solvers. Aims: We study in this paper the prospect of using NN to measure the phasing aberrations of a segmented pupil from the focal-plane image directly without a dedicated wavefront sensing. Methods: In our application, we take the case of a deployable telescope fitting in a CubeSat for Earth observations which triples the aperture size (compared to the 10cm CubeSat standard) and therefore triples the angular resolution capacity. In order to reach the diffraction-limited regime in the visible wavelength, typically, a wavefront error below lambda/50 is required. The telescope focal-plane detector, used for imaging, will be used as a wavefront-sensor. In this work, we study a point source, i.e. the Point Spread Function [PSF] of the optical system as an input of a VGG-net neural network, an architecture designed for image regression/classification. Results: This approach shows some promising results (about 2nm RMS, which is sub lambda/50 of residual WFE with 40-100nm RMS of input WFE) using a relatively fast computational time less than 30 ms which translates a small computation burder. These results allow one further study for higher aberrations and noise.

Keywords: wavefront sensing, deep learning, deployable telescope, space telescope

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Authors: Abdul Halim Abdullah, Nur Liyana Zainal Abidin, Mahani Mokhtar

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Problem-solving is an activity which can encourage students to use Higher Order Thinking Skills (HOTS). Learning fractions can be challenging for students since empirical evidence shows that students experience difficulties in solving the fraction problems. However, visual methods can help students to overcome the difficulties since the methods help students to make meaningful visual representations and link abstract concepts in Mathematics. Therefore, the purpose of this study was to investigate whether there were any changes in students’ HOTS at the four highest levels when learning the fractions by using Thinking Blocks. 54 students participated in a quasi-experiment using pre-tests and post-tests. Students were divided into two groups. The experimental group (n=32) received a treatment to improve the students’ HOTS and the other group acted as the control group (n=22) which used a traditional method. Data were analysed by using Mann-Whitney test. The results indicated that during post-test, students who used Thinking Blocks showed significant improvement in their HOTS level (p=0.000). In addition, the results of post-test also showed that the students’ performance improved significantly at the four highest levels of HOTS; namely, application (p=0.001), analyse (p=0.000), evaluate (p=0.000), and create (p=0.000). Therefore, it can be concluded that Thinking Blocks can effectively encourage students to use the four highest levels of HOTS which consequently enable them to solve fractions problems successfully.

Keywords: Thinking Blocks, Higher Order Thinking Skills (HOTS), fractions, problem solving

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Authors: V.Barbakadze, L.Gogilashvili, L.Amiranashvili, M.Merlani, K.Mulkijanyan

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The high-molecular fractions from the several species of two genera (Symphytum and Anchusa) of Boraginaceae family Symphytum asperum, S. caucasicum, S. officinale, and Anchusa italica were isolated. According to IR, 13C and 1H NMR, 2D heteronuclear 1H/13C HSQC spectral data and 1D NOE experiment, the main structural element of these preparations was found to be a regularly substituted polyoxyethylene, namely poly[3-(3,4-dihydroxyenyl)glyceric acid] (PDPGA) or poly[oxy-1-carboxy-2-(3,4-dihydroxyphenyl)ethylene]. Such caffeic acid-derived biopolymer to our knowledge has not been known and has been identified for the first time. This compound represents a new class of natural polyethers with a residue of 3-(3,4-dihydroxyphenyl)glyceric acid as the repeating unit. Most of the carboxylic groups of PDPGA from A. italica unlike the polymer of S. asperum, S. caucasicum, and S. officinale are methylated. The 2D DOSY experiment gave the similar diffusion coefficient for the methylated and non-methylated signals of A. italica PDPGA. Both sets of signals fell in the same horizontal. This would imply a similar molecular weight for methylated and non-methylated polymers. This was further evidenced by graphic representations of the intensity decay of the 1H signals of aromatic H-2″ and H-1 at δ 7.16 and 5.24 and that of the methoxy group at δ 3.85. These three signals essentially showed the same curve shape. According to results of in vitro and in vivo experiments PDPGA of S.asperum and S.caucasicum could be considered as potential anti-inflammatory, wound healing and anti-cancer therapeutic agent.

Keywords: caffeic acid-derived polyether, poly[3-(3, 4-dihydroxyphenyl)glyceric acid], poly[oxy-1-carboxy-2-(3, 4-dihydroxyphenyl)ethylene], symphytum, anchusa

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Authors: Naeem Ullah, Menglan Duan, Mac Darlington Uche Onuoha

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The lowering of subsea equipment into the deep waters is a challenging job due to the harsh offshore environment. Many researchers have introduced various installation systems to deploy the payload safely into the deep oceans. In general practice, dual floating vessels are not employed owing to the prevalent safety risks and hazards caused by ever-increasing dynamical effects sourced by mutual interaction between the bodies. However, while keeping in the view of the optimal grounds, such as economical one, the Y-method, the two conventional tugboats supporting the equipment by the two independent strands connected to a tri-plate above the equipment, has been employed to study multibody dynamics of the dual barge lifting operations. In this study, the two tugboats and the suspended payload (Y-method) are deployed for the lowering of subsea equipment into the deep waters as a multibody dynamic system. The two-wire ropes are used for the lifting and installation operation by this Y-method installation system. 6-dof (degree of freedom) for each body are considered to establish coupled 18-dof multibody model by embedding technique or velocity transformation technique. The fundamental and prompt advantage of this technique is that the constraint forces can be eliminated directly, and no extra computational effort is required for the elimination of the constraint forces. The inertial frame of reference is taken at the surface of the water as the time-independent frame of reference, and the floating frames of reference are introduced in each body as the time-dependent frames of reference in order to formulate the velocity transformation matrix. The local transformation of the generalized coordinates to the inertial frame of reference is executed by applying the Euler Angle approach. The spherical joints are articulated amongst the multibody as the kinematic joints. The hydrodynamic force, the two-strand forces, the hydrostatic force, and the mooring forces are taken into consideration as the external forces. The radiation force of the hydrodynamic force is obtained by employing the Cummins equation. The wave exciting part of the hydrodynamic force is obtained by using force response amplitude operators (RAOs) that are obtained by the commercial solver ‘OpenFOAM’. The strand force is obtained by considering the wire rope as an elastic spring. The nonlinear hydrostatic force is obtained by the pressure integration technique at each time step of the wave movement. The mooring forces are evaluated by using Faltinsen analytical approach. ‘The Runge Kutta Method’ of Fourth-Order is employed to evaluate the coupled equations of motion obtained for 18-dof multibody model. The results are correlated with the simulated Orcaflex Model. Moreover, the results from Orcaflex Model are compared with the MOSES Model from previous studies. The MBDS of single barge lifting operation from the former studies are compared with the MBDS of the established dual barge lifting operation. The dynamics of the dual barge lifting operation are found larger in magnitude as compared to the single barge lifting operation. It is noticed that the traction at the top connection point of the cable decreases with the increase in the length, and it becomes almost constant after passing through the splash zone.

Keywords: dual barge lifting operation, Y-method, multibody dynamics, shipbuilding, installation of subsea equipment, shipbuilding

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: biometrics, deep learning, handwriting, signature forgery

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Authors: Jason Michael Crozier

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The wrestling territories of the mid-twentieth century in the United States are widely considered the birthplace of modern professional wrestling, and by many professional wrestlers, to be a beacon of hope for the easing of racial tensions during the civil rights era and beyond. The performers writing on this period speak of racial equality but fail to acknowledge the exploitation of black athletes as a racialized capital commodity who suffered the challenges of systemic racism, codified by a false narrative of aspirational exceptionalism and equality measured by audience diversity. The promoters’ ability to equate racial and capital exploitation with equality leads to a broader discussion of the history of Muscular Christianity in the United States and the exploitation of black bodies. Narratives of racial erasure that dominate the historical discourse when examining athleticism and exceptionalism redefined how blackness existed and how physicality and race are conceived of in sport and entertainment spaces. When discussing the implications of race and professional wrestling, it is important to examine the role of promotions as ‘imagined communities’ where the social agency of wrestlers is defined and quantified based on their ‘desired elements’ as a performer. The intentionally vague nature of this language masks a deep history of racialization that has been perpetuated by promoters and never fully examined by scholars. Sympathetic racism and the omission of cultural identity are also key factors in the limitations and racial barriers placed upon black athletes in the squared circle. The use of sympathetic racism within professional wrestling during the twentieth century defined black athletes into two distinct categorizations, the ‘black savage’ or the ‘black minstrel’. Black wrestlers of the twentieth century were defined by their strength as a capital commodity and their physicality rather than their knowledge of the business and in-ring skill. These performers had little agency in their ability to shape their own character development inside and outside the ring. Promoters would often create personas that heavily racialized the performer by tying them to a regional past or memory, such as that of slavery in the deep south using dog collar matches and adoring black characters in chains. Promoters softened cultural memory by satirizing the historic legacy of slavery and the black identity.

Keywords: sympathetic racism, social agency, racial commodification, stereotyping

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Authors: Mai Awad

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This paper aims to focus on the phenomenon of martyrdom—particularly its performative aspect—and how social and popular cultural representations address the multiple meanings of the loaded image of a Palestinian martyr. This focus will help us to explore the possible reasons that might push Palestinians to consider pursuing “martyrdom” or suicide operations. Tracing what happened in the past and what is currently happening (that is, haunting) will aid in theorizing how the act/practice of “martyrdom” is produced. It is believed that there are social and political forces, particularly in a colonial society like Palestine, that influence the subject and its experience. But what is unique about this paper is its attempt to disclose the invisible, hidden narratives and complexities of Palestinian life that we do not see. By giving “martyrs” a chance to speak and express their own narratives—since “martyrs” usually leave written letters for their families, which are published after their death—this study must broaden the whole picture and discuss what is missing. The analytic method to be used: For the methodology, the paper recruits discourse analysis as a method for tracing the emergence, circulation, and productivity of the martyrdom discourse across a range of social practices in Palestinians’ everyday life after the Nakba. The paper analyzes the letters that “martyrs” left to their families, relatives, and the Palestinian community after their death. By letting “martyrs” speak for themselves and hearing their unique discourses, the research would suggest that more explanation is needed to describe the “martyr” identity. Hence, it is not possible to study the “martyr” identity in Palestine without understanding the colonial context that governs it and shapes their subjective experience.

Keywords: martyrdom, palestine, haunting, nakba 1948

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Authors: Elin Linder

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Ethnographic endeavors feature a long history of creative modes of engagements, and anthropology an equally long critique of its disciplinary attention to worded representations of beyond worded experiences. Curious and critical as our research comes about, takes place, unfolds, and develops, processes of documenting, exploring, experiencing, and producing knowledge commonly evolve as intrinsic parts of our situated wishes to make sense of the worlds we study. We may imagine to do one thing and to use a specific mode of fieldnoting, only to end up doing something else, such as to capture dynamics and dimensions otherwise not attentively engaged or even lost. This paper builds on such an experience, and it acts window to open the conversation for doing and representing ethnographic work as creatively as it was undertaken. Expressively and actively undertaken by means of sensuous scholarship, fieldworking in the world of olivicoltura in Apulia intriguingly advanced into resourcefully embodied research using a drone. While the drone first and foremost allowed perspectives that one as a human is largely and physically incapable of exploring, it rapidly emerged into a mode of engagement that probed critical question how one comes to learn how to see that which one watches, listen to that which one hears, smell that which one scents, feel that which one touch, and gather that which one experience. This paper develops how the drone incorporated a transition of a particularly situated ethnographic sense of attention, all while visualizing how imaginative conceptualizations enable unexpected modes of multimodal knowing in much multisensorial worlds of being.

Keywords: drone, multimodality, sensuous scholarship, critical creativity, ethnographic practice

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