Search results for: Deep mixed column

Authors: Van Huong Mai, Kanokwan Nithiratphat, Adool Booncham

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The Movement of Contemporary Life consisted of two purposes, which were to study the movement and development of the modern life and to create the visual arts, which were paintings expressed via the form of apartment buildings was used from mixed media (digital printing and acrylic painting on canvas) which conveyed the rapid pace of modern life leading to diverse movements in viewer’s feeling. The operation of this creation was collected field data, documentary data, and influence from creative work. The data analysis was analyzed in order to theme, form, technique, and process to satisfy of concept and special character of the pieces.

Keywords: movement, contemporary life, visual art, acrylic painting, digital art, urban space

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Authors: Sadia Siddiqa, Z. Khan, M. A. Hossain

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In this article, the effect of thermal radiation on mixed convection boundary layer flow of a viscous fluid along a highly heated vertical flat plate is considered with varying density and volumetric expansion coefficient. The density of the fluid is assumed to vary exponentially with temperature, however; volumetric expansion coefficient depends linearly on temperature. Boundary layer equations are transformed into convenient form by introducing primitive variable formulations. Solutions of transformed system of equations are obtained numerically through implicit finite difference method along with Gaussian elimination technique. Results are discussed in view of various parameters, like thermal radiation parameter, volumetric expansion parameter and density variation parameter on the wall shear stress and heat transfer rate. It is concluded from the present investigation that increase in volumetric expansion parameter decreases wall shear stress and enhances heat transfer rate.

Keywords: thermal radiation, mixed convection, variable density, variable volumetric expansion coefficient

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Authors: P. Paengkoum, S. C. Chen, S. Paengkoum

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Six male growing Thai-indigenous beef cattle with body weight (BW) of 154±13.2 kg were randomly assigned in replicated 3×3 Latin square design, and fed with different levels of crude protein (CP) in total mixed ration (TMR) diets. CP levels in diets were 4.3%, 7.3% and 10.3% base on dry matter (DM). Ruminal ammonia nitrogen (NH3-N) and blood urea nitrogen (BUN) concentrations increased (P<0.01) with increasing CP levels. Moreover, there is a positive relationship between BUN and ruminal NH3-N. Rumen pH, total volatile fatty acid (VFA), molar proportions of acetate, propionate and butyrate were not affected by CP levels (P>0.05).

Keywords: Thai-indigenous beef cattle, crude protein, volatile fatty acid (VFA), total mixed ration (TMR) diets

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Authors: David E. Wilkins

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While colonialism in its many guises remains the dominant theoretical framework within which to analyze Indigenous relations with state powers, this paper takes a deep look at the treaty, policy, and statutory efforts initiated by both Indigenous peoples and colonial, and later federal representatives within what is now the United States that were intended to create a Native constituent state of the union. While these plans ultimately failed, they are indicative of the reality that, throughout much of the shared Indigenous and American history, there were both Native and non-Native political elites who were keen on the idea of incorporating Indigenous peoples into the burgeoning body politic. This paper explores why these plans arose, who the architects were that devised them, which Native peoples were involved in, and why they ultimately failed to be enacted. Although governmental relations within the US remain fraught and unpredictable, Native nations continue to wield a form of sovereignty that, while truncated, has maintained their distinctive political statuses. There is much to be learned from the exploration of these mixed successes and failures. There are other examples across the globe whereby Indigenous peoples like the Saami and the Māori have secured greater clarity of their retained autonomy through structural political arrangements with the states that have laid claim to their territories while the Ainu struggle to regain their status.

Keywords: indigenous, sovereignty, diplomacy, intergovernmental

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Authors: R. Alias, A. Kasa, M. R. Taha

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The effect of particle size on shear strength of granular materials are investigated using direct shear tests. Small direct shear test (60 mm by 60 mm by 24 mm deep) were conducted for particles passing the sieves with opening size of 2.36 mm. Meanwhile, particles passing the standard 20 mm sieves were tested using large direct shear test (300 mm by 300 mm by 200 mm deep). The large direct shear tests and the small direct shear tests carried out using the same shearing rate of 0.09 mm/min and similar normal stresses of 100, 200, and 300 kPa. The results show that the peak and residual shear strength decreases as particle size increases.

Keywords: particle size, shear strength, granular material, direct shear test

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

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

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

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Smita Nepal

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Despite being on the global education reform agenda for over two decades, interpretations and practices of inclusive education vary widely across the world. In Nepal, similar to many other developing countries, inclusive education is still an emerging concept and limited research is available to date in relation to how inclusive education is conceptualized and implemented here. Moreover, very little is known about how teachers who are at the frontline of providing inclusive education understand this concept and how well they are prepared to teach inclusively. This study addresses this research gap by investigating an overarching research question, ‘How prepared are Nepalese teachers to practice inclusive pedagogy?’ Different societies and cultures may have different interpretations of the concepts of diversity and inclusion. Acknowledging that such contextual differences influence how these issues are addressed, such as preparing teachers for providing inclusive education, this study has investigated the research questions using a sociocultural conceptual framework. A sequential mixed-method research design involved quantitative data from 203 survey responses collected in the first phase, followed by qualitative data in the second phase collected through semi-structured interviews with teachers. Descriptive analysis of the quantitative data and reflexive thematic analysis of the qualitative data revealed a narrow understanding of inclusive education in the participating Nepalese teachers with limited preparedness for implementing inclusive pedagogy. Their interpretation of inclusion substantially included the need for non-discrimination and the provision of equal opportunities. This interpretation was found to be influenced by the social context where a lack of a deep understanding of human diversity was reported, leading to discriminatory attitudes and practices. In addition, common norms established in society that experiencing privileges or disadvantages was normal for diverse groups of people appeared to have led to limited efforts to enhance teachers’ understanding of and preparedness for inclusive education. This study has significant implications, not only in the Nepalese context but globally, for reform in policies and practices and for strengthening the teacher education and professional development system to promote inclusion in education. In addition, the significance of this research lies in highlighting the importance of further context-specific research in this area to ensure inclusive education in a real sense by valuing socio-cultural differences.

Keywords: inclusive education, inclusive pedagogy, sociocultural context, teacher preparation

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

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Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

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Authors: Ehsan Heidaryan

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Gas hydrate sediments are ice like permafrost in deep see and oceans. Methane production in sequestration process and reducing atmospheric carbon dioxide, a main source of greenhouse gas, has been accentuated recently. One focus is capture, separation, and sequestration of industrial carbon dioxide. As a hydrate former, carbon dioxide forms hydrates at moderate temperatures and pressures. This phenomenon could be utilized to capture and separate carbon dioxide from flue gases, and also has the potential to sequester carbon dioxide in the deep seabeds. This research investigated the effect of synthetic surfactants on carbon dioxide hydrate formation, catalysis and consequently, methane production from hydrate permafrosts in sediments. It investigated the sequestration potential of carbon dioxide hydrates in ocean sediments. Also, the catalytic effect of biosurfactants in these processes was investigated.

Keywords: carbon dioxide, hydrate, sequestration, surfactant

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Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf

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Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.

Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks

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Authors: German Galvez-Garcia, Javier Albayay, Javiera Peña, Marta Lavin, George A. Michael

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The aim of this research was to investigate whether the selection of the actions needs different inhibition processes during the response selection stage. In Experiment 1, we compared the magnitude of the Spatial Stroop effect, which occurs in response selection stage, in two motor actions (lifting vs reaching) when the participants performed both actions in the same block or in different blocks (mixed block vs. pure blocks).Within pure blocks, we obtained faster latencies when lifting actions were performed, but no differences in the magnitude of the Spatial Stroop effect were observed. Within mixed block, we obtained faster latencies as well as bigger-magnitude for Spatial Stroop effect when reaching actions were performed. We concluded that when no action selection is required (the pure blocks condition), inhibition works as a unitary system, whereas in the mixed block condition, where action selection is required, different inhibitory processes take place within a common processing stage. In Experiment 2, we investigated this common processing stage in depth by limiting participants’ available resources, requiring them to engage in a concurrent auditory task within a mixed block condition. The Spatial Stroop effect interacted with Movement as it did in Experiment 1, but it did not significantly interact with available resources (Auditory task x Spatial Stroop effect x Movement interaction). Thus, we concluded that available resources are distributed equally to both inhibition processes; this reinforces the likelihood of there being a common processing stage in which the different inhibitory processes take place.

Keywords: inhibition process, motor processes, selective inhibition, dual task

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Authors: Min Ho Kwon, Woo Young Jung, Hyun Su Seo

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A Polymer Cement Mortar (PCM) has been widely used as the material of repair and restoration work for concrete structure; however a PCM usually induces an environmental pollutant. Therefore, there is a need to develop PCM which is less impact to environments. Usually, UM resin is known to be harmless to the environment. Accordingly, in this paper, the properties of the PCM using UM resin were studied. The general cement mortar and UM resin was mixed in the specified ratio. A certain percentage of PVA fibers, steel fibers and mixed fibers (PVA fiber and steel fiber) were added to enhance the flexural strength. The flexural tests were performed in order to investigate the flexural strength of each PCM. Experimental results showed that the strength of proposed PCM using UM resin is improved when they are compared with general cement mortar.

Keywords: polymer cement mortar, UM resin, compressive strength, PVA fiber, steel fiber

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Carmela Perta

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Among the studies flourished in the field of historical sociolinguistics, mainly in the strand devoted to English history, during its Medieval and early modern phases, multilingual texts had been analysed using theories and models coming from contact linguistics, thus applying synchronic models and approaches to the past. This is true also in the case of contact phenomena which would transcend the writing level involving the language systems implicated in contact processes to the point of perceiving a new variety. This is the case for medieval administrative-commercial texts in which, according to some Scholars, the degree of fusion of Anglo-Norman, Latin and middle English is so high a mixed code emerges, and there are recurrent patterns of mixed forms. Interesting is a collection of multilingual business writings by John Balmayn, an Englishman overseeing a large shipment in Tuscany, namely the Cantelowe accounts. These documents display various analogies with multilingual texts written in England in the same period; in fact, the writer seems to make use of the above-mentioned patterns, with Middle English, Latin, Anglo-Norman, and the newly added Italian. Applying an atomistic yet dynamic approach to the study of contact phenomena, we will investigate these documents, trying to explore the nature of the switching forms they contain from an intra-writer variation perspective. After analysing the accounts and the type of multilingualism in them, we will take stock of the assumed mixed code nature, comparing the characteristics found in this genre with modern assumptions. The aim is to evaluate the possibility to consider the switching forms as core elements of a mixed code, used as professional variety among merchant communities, or whether such texts should be analysed from a switching perspective.

Keywords: historical sociolinguistics, historical code switching, letters, medieval england

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Authors: S. Mohamed, D. Gonzalez, C. Sayada, P. Halfon

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Drug resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance (DR) interpretations has not yet been studied. Fifty HIV-1 patients who experienced virological failure were included in this retrospective study. The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, E, F, and G. DeepChek®-HIV simplified DR interpretation software was used to compare Sanger sequencing and UDS. The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of DR revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with > 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the DR interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences. A combination of UDS and DeepChek® software for the interpretation of DR results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterisation of the viral population by identifying additional resistance mutations and improving the DR interpretation.

Keywords: HIV-1, ultra-deep sequencing, Sanger sequencing, drug resistance

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Authors: Hari Krishan Sharma, Sanjay Kumar Sharma, Sushil Kumar Swar

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Inadequate transverse reinforcement is considered as the main reason for the beam column joint shear failure observed during recent earthquakes. DSP matrix consists of cement and high content of micro-silica with low water to cement ratio while the aggregates are graded quartz sand. The use of reinforcing fibres leads not only to the increase of tensile/bending strength and specific fracture energy, but also to reduction of brittleness and, consequently, to production of non-explosive ruptures. Besides, fibre-reinforced materials are more homogeneous and less sensitive to small defects and flaws. Recent works on the freeze-thaw durability (also in the presence of de-icing salts) of fibre-reinforced DSP confirm the excellent behaviour in the expected long term service life.DSP materials, including fibre-reinforced DSP and CRC (Compact Reinforced Composites) are obtained by using high quantities of super plasticizers and high volumes of micro-silica. Steel fibres with high tensile yield strength of smaller diameter and short length in different fibre volume percentage and aspect ratio tilized to improve the performance by reducing the brittleness of matrix material. In the case of High Performance Densified Small Particle Concrete (HPDSPC), concrete is dense at the micro-structure level, tensile strain would be much higher than that of the conventional SFRC, SIFCON & SIMCON. Beam-column sub-assemblages used as moment resisting constructed using HPDSPC in the joint region with varying quantities of steel fibres, fibre aspect ratio and fibre orientation in the critical section. These HPDSPC in the joint region sub-assemblages tested under cyclic/earthquake loading. Besides loading measurements, frame displacements, diagonal joint strain and rebar strain adjacent to the joint will also be measured to investigate stress-strain behaviour, load deformation characteristics, joint shear strength, failure mechanism, ductility associated parameters, stiffness and energy dissipated parameters of the beam column sub-assemblages also evaluated. Finally a design procedure for the optimum design of HPDSPC corresponding to moment, shear forces and axial forces for the reinforced concrete beam-column joint sub-assemblage proposed. The fact that the implementation of material brittleness measure in the design of RC structures can improve structural reliability by providing uniform safety margins over a wide range of structural sizes and material compositions well recognized in the structural design and research. This lead to the development of high performance concrete for the optimized combination of various structural ratios in concrete for the optimized combination of various structural properties. The structural applications of HPDSPC, because of extremely high strength, will reduce dead load significantly as compared to normal weight concrete thereby offering substantial cost saving and by providing improved seismic response, longer spans, and thinner sections, less reinforcing steel and lower foundation cost. These cost effective parameters will make this material more versatile for use in various structural applications like beam-column joints in industries, airports, parking areas, docks, harbours, and also containers for hazardous material, safety boxes and mould & tools for polymer composites and metals.

Keywords: high performance densified small particle concrete (HPDSPC), steel fibre reinforced concrete (SFRC), slurry infiltrated concrete (SIFCON), Slurry infiltrated mat concrete (SIMCON)

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Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng

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Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.

Keywords: human perception, public space quality, deep learning, negative elements, street images

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: A. Aslihan Gokaltun, Ali Tuncel

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The unique properties of capillary electrochromatography (CEC) such as high performance, high selectivity, low consumption of both reagents and analytes ensure this technique an attractive one for the separation of biomolecules including nucleosides and nucleotides, peptides, proteins, carbohydrates. Monoliths have become a well-established separation media for CEC in the format that can be compared to a single large 'particle' that does not include interparticular voids. Convective flow through the pores of monolith significantly accelerates the rate of mass transfer and enables a substantial increase in the speed of the separation. In this work, we propose a new approach for the preparation of cationic monolithic stationary phase for capillary electrochromatography. Instead of utilizing a charge bearing monomer during polymerization, the desired charge-bearing group is generated on the capillary monolith after polymerization by using the reactive moiety of the monolithic support via one-pot, simple reaction. Optimized monolithic column compensates the disadvantages of frequently used reversed phases, which are difficult for separation of polar solutes. Rapid separation and high column efficiencies are achieved for the separation of neutral analytes, nucleic acid bases and nucleosides in reversed phase mode. Capillary monolith showed satisfactory hydrodynamic permeability and mechanical stability with relative standard deviation (RSD) values below 2 %. A new promising, reactive support that has a 'ligand selection flexibility' due to its reactive functionality represent a new family of separation media for CEC.

Keywords: biomolecules, capillary electrochromatography, cationic monolith, neutral analytes

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Authors: Shri Krishna Mishra

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In an educational context, technology can prompt interactive plight only when it is used in conjunction with interactive plight methods. This study, therefore, examines the relationships among components that contribute to higher levels of interactive plight and execution, such as interactive Plight methods, technology, intrinsic motivation and deep learning. 526 students participated in this study. With structural equation modelling, the authors test the conceptual model and identify satisfactory model fit. The results indicate that interactive Plight methods, technology and intrinsic motivation have significant relationship with interactive Plight; deep learning mediates the relationships of the other variables with Execution.

Keywords: searching the relationship among components, contribute to interactive plight, educational execution, intrinsic motivation

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Authors: Ghada A. Abdelbary, Maha M. Amin, Mostafa Abdelmoteleb

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Zaleplon (ZP) is a non-benzodiazepine poorly soluble hypnotic drug indicated for the short term treatment of insomnia having a bioavailability of about 30%. The aim of the present study is to enhance the solubility and consequently the bioavailability of ZP using hydrotropic agents (HA). Phase solubility diagrams of ZP in presence of different molar concentrations of HA (Sodium benzoate, Urea, Ascorbic acid, Resorcinol, Nicotinamide, and Piperazine) were constructed. ZP/Sodium benzoate and Resorcinol microparticles were prepared adopting melt, solvent evaporation and melt-evaporation techniques followed by XRD. Directly compressed mixed hydrotropic ZP tablets of Sodium benzoate and Resorcinol in different weight ratios were prepared and evaluated compared to the commercially available tablets (Sleep aid® 5 mg). The effect of shelf and accelerated stability storage (40°C ± 2°C/75%RH ± 5%RH) on the optimum tablet formula (F5) for six months were studied. The enhancement of ZP solubility follows the order of: Resorcinol > Sodium benzoate > Ascorbic acid > Piperazine > Urea > Nicotinamide with about 350 and 2000 fold increase using 1M of Sodium benzoate and Resorcinol respectively. ZP/HA microparticles exhibit the order of: Solvent evaporation > melt-solvent evaporation > melt > physical mixture which was further confirmed by the complete conversion of ZP into amorphous form. Mixed hydrotropic tablet formula (F5) composed of ZP/(Resorcinol: Sodium benzoate 4:1w/w) microparticles prepared by solvent evaporation exhibits in-vitro dissolution of 31.7±0.11% after five minutes (Q5min) compared to 10.0±0.10% for Sleep aid® (5 mg) respectively. F5 showed significantly higher GABA concentration of 122.5±5.5mg/mL in plasma compared to 118±1.00 and 27.8±1.5 mg/mL in case of Sleep aid® (5 mg) and control taking only saline respectively suggesting a higher neuropharmacological effect of ZP following hydrotropic solubilization.

Keywords: zaleplon, hydrotropic solubilization, plasma GABA level, mixed hydrotropy

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Authors: Hiba S. Ahmed, Abbas A. Allawi, Riyadh A. Hindi

Abstract:

Pultruded materials made of fiber-reinforced polymer (FRP) come in a broad range of shapes, such as bars, I-sections, C-sections, and other structural sections. These FRP materials are starting to compete with steel as structural materials because of their great resistance, low self-weight, and cheap maintenance costs-especially in corrosive conditions. This study aimed to evaluate the effectiveness of Glass Fiber Reinforced Polymer (GFRP) of the hybrid columns built by combining (GFRP) profiles with concrete columns because of their low cost and high structural efficiency. To achieve the aims of this study, nine circular columns with a diameter of (150 mm) and a height of (1000mm) were cast using normal concrete with compression strength equal to (35 MPa). The research involved three different types of reinforcement: hybrid circular columns type (IG) with GFRP I-section and 1% of the reinforcement ratio of steel bars, hybrid circular columns type (IS) with steel I-section and 1% of the reinforcement ratio of steel bars, (where the cross-section area of I-section for GFRP and steel was the same), compared with reference column (R) without I-section. To investigate the ultimate capacity, axial and lateral deformation, strain in longitudinal and transverse reinforcement, and failure mode of the circular column under different loading conditions (concentric and eccentric) with eccentricities of 25 mm and 50 mm, respectively. In the second part, an analytical finite element model will be performed using ABAQUS software to validate the experimental results.

Keywords: composite, columns, reinforced concrete, GFRP, axial load

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Authors: Prathiksha Kamath, Pratibha Jamkhandi, Prateek Ghanti, Priyanshu Gupta, M. Lakshmi Neelima

Abstract:

Combining Image Captioning and Visual Question Answering (VQA) tasks have emerged as a new and exciting research area. The image captioning task involves generating a textual description that summarizes the content of the image. VQA aims to answer a natural language question about the image. Both these tasks include computer vision and natural language processing (NLP) and require a deep understanding of the content of the image and semantic relationship within the image and the ability to generate a response in natural language. There has been remarkable growth in both these tasks with rapid advancement in deep learning. In this paper, we present a comprehensive review of recent progress in combining image captioning and visual question-answering (VQA) tasks. We first discuss both image captioning and VQA tasks individually and then the various ways in which both these tasks can be integrated. We also analyze the challenges associated with these tasks and ways to overcome them. We finally discuss the various datasets and evaluation metrics used in these tasks. This paper concludes with the need for generating captions based on the context and captions that are able to answer the most likely asked questions about the image so as to aid the VQA task. Overall, this review highlights the significant progress made in combining image captioning and VQA, as well as the ongoing challenges and opportunities for further research in this exciting and rapidly evolving field, which has the potential to improve the performance of real-world applications such as autonomous vehicles, robotics, and image search.

Keywords: image captioning, visual question answering, deep learning, natural language processing

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

Abstract:

The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Mst Shapna Akter, Hossain Shahriar

Abstract:

One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

Procedia PDF Downloads 88