Search results for: soft classifiers

Authors: Heitor Oliveira, Gabriele De-Waal, Juergen Schmenger, Lynsey Godfrey, Tibor Kovacs

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Rheolaser MASTER™ makes use of multiple scattering of light, caused by scattering objects in a continuous medium (such as droplets and particles in colloids), to characterize the viscoelasticity of soft materials. It offers an alternative to conventional rheometers to characterize viscoelasticity of products such as hair cosmetics. Up to six simultaneous measurements at controlled temperature can be carried out simultaneously (10-15 min), and the method requires only minor sample preparation work. Conversely to conventional rheometer based methods, no mechanical stress is applied to the material during the measurements. Therefore, the properties of the exact same sample can be monitored over time, like in aging and stability studies. We determined the elastic index (EI) of water/emulsion mixtures (1 ≤ fat alcohols (FA) ≤ 5 wt%) and emulsion/gel-network mixtures (8 ≤ FA ≤ 17 wt%) and compared with the elastic/sorage mudulus (G’) for the respective samples using a TA conventional rheometer with flat plates geometry. As expected, it was found that log(EI) vs log(G’) presents a linear behavior. Moreover, log(EI) increased in a linear fashion with solids level in the entire range of compositions (1 ≤ FA ≤ 17 wt%), while rheometer measurements were limited to samples down to 4 wt% solids level. Alternatively, a concentric cilinder geometry would be required for more diluted samples (FA > 4 wt%) and rheometer results from different sample holder geometries are not comparable. The plot of the rheolaser output parameters solid-liquid balance (SLB) vs EI were suitable to monitor product aging processes. These data could quantitatively describe some observations such as formation of lumps over aging time. Moreover, this method allowed to identify that the different specifications of a key raw material (RM < 0.4 wt%) in the respective gel-network (GN) product has minor impact on product viscoelastic properties and it is not consumer perceivable after a short aging time. Broadening of a RM spec range typically has a positive impact on cost savings. Last but not least, the photon path length (λ*)—proportional to droplet size and inversely proportional to volume fraction of scattering objects, accordingly to the Mie theory—and the EI were suitable to characterize product destabilization processes (e.g., coalescence and creaming) and to predict product stability about eight times faster than our standard methods. Using these parameters we could successfully identify formulation and process parameters that resulted in unstable products. In conclusion, Rheolaser allows quick and reliable characterization of viscoelastic properties of hair cosmetics that are related to their performance and stability. It operates in a broad range of product compositions and has applications spanning from the formulation of our hair cosmetics to fast release criteria in our production sites. Last but not least, this powerful tool has positive impact on R&D development time—faster delivery of new products to the market—and consequently on cost savings.

Keywords: colloids, hair cosmetics, light scattering, performance and stability, soft materials, viscoelastic properties

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Authors: Shih-Yu Wang, Shun-Wen Hsiao

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In the field of cybersecurity, there exists many vendors giving malware samples classified results, namely naming after the label that contains some important information which is also called AV label. Lots of researchers relay on AV labels for research. Unfortunately, AV labels are too cluttered. They do not have a fixed format and fixed naming rules because the naming results were based on each classifiers' viewpoints. A way to fix the problem is taking a majority vote. However, voting can sometimes create problems of bias. Thus, we create a novel ensemble approach which does not rely on the cacophonous naming result but depend on group identification to aggregate everyone's opinion. To achieve this purpose, we develop an scoring system called Pairwise Consensus Score (PCS) to calculate result similarity. The entire method architecture combine Genetic Algorithm and PCS to find maximum consensus in the group. Experimental results revealed that our method outperformed the majority voting by 10% in term of the score.

Keywords: genetic algorithm, ensemble learning, malware family, malware labeling, AV labels

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Authors: Mannika Garg, N. Srinivas Murthy, Sunish Nair

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TeamIndus is only Indian team participated in the Google Lunar X Prize (GLXP). GLXP is an incentive prize space competition which is organized by the XPrize Foundation and sponsored by Google. The main objective of the mission is to soft land a rover on the moon surface, travel minimum displacement of 500 meters and transmit HD and NRT videos and images to the Earth. Team Indus is designing a Lunar Lander which carries Rover with it and deliver onto the surface of the moon with a soft landing. For lander to survive throughout the mission, energy is required to operate all attitude control sensors, actuators, heaters and other necessary components. Photovoltaic solar array systems are the most common and primary source of power generation for any spacecraft. The scope of this paper is to provide a system-level approach for designing the solar array systems of the lander to generate required power to accomplish the mission. For this mission, the direction of design effort is to higher efficiency, high reliability and high specific power. Towards this approach, highly efficient multi-junction cells have been considered. The design is influenced by other constraints also like; mission profile, chosen spacecraft attitude, overall lander configuration, cost effectiveness and sizing requirements. This paper also addresses the various solar array design challenges such as operating temperature, shadowing, radiation environment and mission life and strategy of supporting required power levels (peak and average). The challenge to generate sufficient power at the time of surface touchdown, due to low sun elevation (El) and azimuth (Az) angle which depends on Lunar landing site, has also been showcased in this paper. To achieve this goal, energy balance analysis has been carried out to study the impact of the above-mentioned factors and to meet the requirements and has been discussed in this paper.

Keywords: energy balance analysis, multi junction solar cells, photovoltaic, reliability, spacecraft attitude

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Authors: Ammar Edress Mohamed, Mustafa Aziz, David Wright

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This paper analyses and calculates the head fields of asymmetrical 2D magnetic recording heads when the soft-underlayer is present using the appropriate Green's function to derive the surface potential/field by utilising the surface potential for asymmetrical head without underlayer. The results follow closely the corners, while the gap region shows a linear behaviour for d/g < 0.5 compared with the calculated fields from finite-element.

Keywords: magnetic recording, finite elements, asymmetrical magnetic heads, superposition, Laplace's equation

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

Procedia PDF Downloads 151

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Anita Kushwaha

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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Daniela Velichová

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Plenty of ideas on how to adopt active learning methods in education are available nowadays. Mathematics is a subject where the active involvement of students is required in particular in order to achieve desirable results regarding sustainable knowledge and deep understanding. The present article is based on the outcomes of an Erasmus+ project DrIVE-MATH, that was aimed at developing a novel and integrated framework to teach maths classes in engineering courses at the university level. It is fundamental for students from the early years of their academic life to have agile minds. They must be prepared to adapt to their future working environments, where enterprises’ views are always evolving, where all collaborate in teams, and relations between peers are thought for the well-being of the whole - workers and company profit. This reality imposes new requirements on higher education in terms of adaptation of different pedagogical methods, such as project-based and active-learning methods used within the course curricula. Active learning methodologies are regarded as an effective way to prepare students to meet the challenges posed by enterprises and to help them in building critical thinking, analytic reasoning, and insight to the solved complex problems from different perspectives. Fostering learning-by-doing activities in the pedagogical process can help students to achieve learning independence, as they could acquire deeper conceptual understanding by experimenting with the abstract concept in a more interesting, useful, and meaningful way. Clear information about learning outcomes and goals might help students to take more responsibility for their learning results. Active learning methods implemented by the project team members in their teaching practice, eduScrum and Jigsaw in particular, proved to provide better scientific and soft skills support to students than classical teaching methods. EduScrum method enables teachers to generate a working environment that stimulates students' working habits and self-initiative as they become aware of their responsibilities within the team, their own acquired knowledge, and their abilities to solve problems independently, though in collaboration with other team members. This method enhances collaborative learning, as students are working in teams towards a common goal - knowledge acquisition, while they are interacting with each other and evaluated individually. Teams consisting of 4-5 students work together on a list of problems - sprint; each member is responsible for solving one of them, while the group leader – a master, is responsible for the whole team. A similar principle is behind the Jigsaw technique, where the classroom activity makes students dependent on each other to succeed. Students are divided into groups, and assignments are split into pieces, which need to be assembled by the whole group to complete the (Jigsaw) puzzle. In this paper, analysis of students’ perceptions concerning the achievement of deeper conceptual understanding in mathematics and the development of soft skills, such as self-motivation, critical thinking, flexibility, leadership, responsibility, teamwork, negotiation, and conflict management, is presented. Some new challenges are discussed as brought by introducing active learning methods in the basic mathematics courses. A few examples of sprints developed and used in teaching basic maths courses at technical universities are presented in addition.

Keywords: active learning methods, collaborative learning, conceptual understanding, eduScrum, Jigsaw, soft skills

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Authors: Sandra Ghavam Shirazi, Mohsen Ramezan Shirazi, Mohammadreza Golhashem

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Nowadays by development of construction in modern world new techniques are introduced to civil engineering. As for geotechnical problems and demands of soil improvement, engineers are searching for decisive methods to ensure the safety of projects. As a popular material Geotextiles are used in almost every aspect of civil engineering. There is a vast variety of geotextiles and each kind has their own unique characteristics therefor to select the proper geotextile for a specific project their properties must be carefully examined. This review gathers and evaluates different parameters of geotextiles that are used in geotechnical field.

Keywords: geotextile, soft soils, fabric, stabilization, fiber

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Authors: Ann Devitt, Mairead Brady, Markus Lamest, Stephen Gomez

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This study set out to explore the use of an online collaborative serious game for student learning in a postgraduate introductory marketing module. The simulation game aimed to bridge the theory-practice divide in marketing by allowing students to apply theory in a safe, simulated marketplace. This study addresses the following research questions: Does an online marketing simulation game engage students higher order cognitive skills? Does collaborative activity required develop students’ “soft” skills, such as communication and negotiation? What specific affordances of the online simulation promote learning? This qualitative case study took place in 2014 with 40 postgraduate students on a Business Masters Programme. The two-week intensive module combined lectures with collaborative activity on a marketing simulation game, MMX from Pearsons. The game requires student teams to compete against other teams in a marketplace and design a marketing plan to maximize key performance indicators. The data for this study comprise essays written by students after the module reflecting on their learning on the module. A thematic analysis was conducted of the essays using the following a priori theme sets: 6 levels of the cognitive domain of Blooms taxonomy; 5 principles of Cooperative Learning; affordances of simulation environments including experiential learning; motivation and engagement; goal orientation. Preliminary findings would strongly suggest that the game facilitated students identifying the value of theory in practice, in particular for future employment; enhanced their understanding of group dynamics and their role within that; and impacted very strongly, both positively and negatively on motivation. In particular the game mechanics of MMX, which hinges on the correct identification of a target consumer group, was identified as a key determinant of extrinsic and intrinsic motivation for learners. The findings also suggest that the situation of the simulation game within a broader module which required post-game reflection was valuable in identifying key learning of marketing concepts in both the positive and the negative experiences of the game.

Keywords: simulation, marketing, serious game, cooperative learning, bloom's taxonomy

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Authors: S. Thomet, S. De-Paoli, F. Ghaffari, J. M. Daveau, P. Roche, O. Romain

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In this paper, we present a hardware module dedicated to understanding the fail reason of a System on Chip (SoC) exposed to a particle beam. Impact of Single-Event Effects (SEE) on processor-based SoCs is a concern that has increased in the past decade, particularly for terrestrial applications with automotive safety increasing requirements, as well as consumer and industrial domains. The SEE created by the impact of a particle on an SoC may have consequences that can end to instability or crashes. Specific hardening techniques for hardware and software have been developed to make such systems more reliable. SoC is then qualified using cosmic ray Accelerated Soft-Error Rate (ASER) to ensure the Soft-Error Rate (SER) remains in mission profiles. Understanding where errors are occurring is another challenge because of the complexity of operations performed in an SoC. Common techniques to monitor an SoC running under a beam are based on non-intrusive debug, consisting of recording the program counter and doing some consistency checking on the fly. To detect and understand SEE, we have developed a module embedded within the SoC that provide support for recording probes, hardware watchpoints, and a memory mapped register bank dedicated to software usage. To identify CPU failure modes and the most important resources to probe, we have carried out a fault injection campaign on the RTL model of the SoC. Probes are placed on generic CPU registers and bus accesses. They highlight the propagation of errors and allow identifying the failure modes. Typical resulting errors are bit-flips in resources creating bad addresses, illegal instructions, longer than expected loops, or incorrect bus accesses. Although our module is processor agnostic, it has been interfaced to a RISC-V by probing some of the processor registers. Probes are then recorded in a ring buffer. Associated hardware watchpoints are allowing to do some control, such as start or stop event recording or halt the processor. Finally, the module is also providing a bank of registers where the firmware running on the SoC can log information. Typical usage is for operating system context switch recording. The module is connected to a dedicated debug bus and is interfaced to a remote controller via a debugger link. Thus, a remote controller can interact with the monitoring module without any intrusiveness on the SoC. Moreover, in case of CPU unresponsiveness, or system-bus stall, the recorded information can still be recovered, providing the fail reason. A preliminary version of the module has been integrated into a test chip currently being manufactured at ST in 28-nm FDSOI technology. The module has been triplicated to provide reliable information on the SoC behavior. As the primary application domain is automotive and safety, the efficiency of the module will be evaluated by exposing the test chip under a fast-neutron beam by the end of the year. In the meantime, it will be tested with alpha particles and electromagnetic fault injection (EMFI). We will report in the paper on fault-injection results as well as irradiation results.

Keywords: fault injection, SoC fail reason, SoC soft error rate, terrestrial application

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Authors: Ibtihal D. Mustafa, Mawia A. Hassan

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Tumor segmentation from MRI image is important part of medical images experts. This is particularly a challenging task because of the high assorting appearance of tumor tissue among different patients. MRI images are advance of medical imaging because it is give richer information about human soft tissue. There are different segmentation techniques to detect MRI brain tumor. In this paper, different procedure segmentation methods are used to segment brain tumors and compare the result of segmentations by using correlation and structural similarity index (SSIM) to analysis and see the best technique that could be applied to MRI image.

Keywords: MRI, segmentation, correlation, structural similarity

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Authors: Amit Verma, Simranjeet Kaur, Sandeep Kaur

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Test Case Prioritization (TCP) has gained wide spread acceptance as it often results in good quality software free from defects. Due to the increase in rate of faults in software traditional techniques for prioritization results in increased cost and time. Main challenge in TCP is difficulty in manually validate the priorities of different test cases due to large size of test suites and no more emphasis are made to make the TCP process automate. The objective of this paper is to detect the priorities of different test cases using an artificial neural network which helps to predict the correct priorities with the help of back propagation algorithm. In our proposed work one such method is implemented in which priorities are assigned to different test cases based on their frequency. After assigning the priorities ANN predicts whether correct priority is assigned to every test case or not otherwise it generates the interrupt when wrong priority is assigned. In order to classify the different priority test cases classifiers are used. Proposed algorithm is very effective as it reduces the complexity with robust efficiency and makes the process automated to prioritize the test cases.

Keywords: test case prioritization, classification, artificial neural networks, TF-IDF

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Payel Datta, Abhishek Das, Abhishek Roychoudhury, Dhiman Chattopadhyay, Tanushyam Chattopadhyay

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Machine learning (ML) and deep learning (DL) is most predominantly used in image/video processing, natural language processing (NLP), audio and speech recognition but not that much used in system performance evaluation. In this paper, authors are going to describe the architecture of an abstraction layer constructed using ML/DL to detect the system failure. This proposed system is used to detect the system failure by evaluating the performance metrics of an IoT service deployment under constrained infrastructure environment. This system has been tested on the manually annotated data set containing different metrics of the system, like number of threads, throughput, average response time, CPU usage, memory usage, network input/output captured in different hardware environments like edge (atom based gateway) and cloud (AWS EC2). The main challenge of developing such system is that the accuracy of classification should be 100% as the error in the system has an impact on the degradation of the service performance and thus consequently affect the reliability and high availability which is mandatory for an IoT system. Proposed ML/DL classifiers work with 100% accuracy for the data set of nearly 4,000 samples captured within the organization.

Keywords: machine learning, system performance, performance metrics, IoT, edge

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Authors: Jadisha Cornejo, Helio Pedrini

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Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Keywords: emotion recognition, facial expression, occlusion, fiducial landmarks

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Authors: Joshua van Staden, Dane Brown, Karen Bradshaw

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Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data.

Keywords: feature engineering, human pose, machine learning, security

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Authors: Hamitri-Guerfi Fatiha, Madani Khodir, Hadjal Samir, Kati Djamel, Youyou Ahcene

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Date palm (Phoenix dactylifera) is a principal fruit that is grown in many regions of the world, resulting in a surplus production of dates. Algeria is considered to be one of the date producing countries. Date seeds (pits) have been a problem to the date industry as a waste stream. However, finding a way to make a profit on the pits would benefit date farmers substantially. This work concentrated on the valorization of date seed oils. A preliminary study was carried out on three varieties (soft, half soft, and dry) and we selected the dry variety. This work concerns the valorization of the date seed oil of the dry variety: ‘Mech Degla’ by its incorporation in a food formulation: margarine of table. Lipid extraction was carried out by hot extraction with the soxhlet; the extracts obtained are rich in fat contents, the results gave outputs of 13.21±0.21 %. The antioxidant activity of extracted oils was studied by the test of DPPH, the content polyphenols as well as the anti-radicalaire activity. The analysis of fatty acids was made by CPG. Thus, it comes out from our results that the recovered fat contents are interesting and considerable. A formulation of the margarine ‘BIO’ was elaborated on the scale industrialist by the addition of the extracts of date seeds ‘Mech-Degla’ oil in order to substitute a synthetic additive. The physicochemical characteristics of the elaborate margarines prove to be in conformity with the standards set by the Algerian companies. The texture of the elaborate margarine has an acceptable color, an aspect brilliant and homogeneous, it is plastic and easy to paste having an index of required SFC and the margarine melts easily in the mouth. Moreover, the evaluation of oxidative stability is carried out by the test of Rancimat. The result obtained reported that the margarine enriched with date seed oil, proved more resistant to oxidation, than the margarine without extract, which is improved much during incorporation of the extracts simultaneously. By conclusion, considering the content of polyphénols noted in the two extracts (aqueous and oily), we can exhort the scientific community to become aware of the treasures of our country especially the wonders of the south which are the dates and theirs under products (pits).

Keywords: antioxydant activity, date seed oil, quality characteristics, margarine

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Authors: Amna Khaliq

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In this article, the phenomena of common stereotypes and glass ceiling barriers in women’s career advancement in men dominating society are explored. A brief background is provided on the misconception for women as soft, delicate, polite and compassionate at a workplace in the place of strong head and go-getter. Then, the literature review supports that stereotypes and glass ceiling barriers are still in existence for young women’s leadership. Increased encouragement, emotional intelligence, and better communication skills are recommended to parents, educators, and employers to prepare young women for senior leadership roles. Young women need mentorship from other women with no competition.

Keywords: Gender inequality, Glass ceiling, Stereotypes, Leadership

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Authors: Hema V. Vanar, R. K. Soni, N. D. Shah

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Slabs supported directly on columns without beams are known as Flat slabs. Flat slabs are highly versatile elements widely used in construction, providing minimum depth, fast construction and allowing flexible column grids. The main objective of this thesis is comparison of analytical method and soft ware for analysis of flat slab subjected to various parametric loadings. Study presents analysis of flat slab is performed under different types of gravity.

Keywords: fat slab, parametric load, analysis, software

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Authors: Qazi Umer Jamil, Abubakr Siddique, Mubeen Ur Rehman, Nida Aziz, Mohsin I. Tiwana

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This paper discusses a brain controlled robotic gait trainer for neurorehabilitation of Spinal Cord Injury (SCI) patients. Patients suffering from Spinal Cord Injuries (SCI) become unable to execute motion control of their lower proximities due to degeneration of spinal cord neurons. The presented approach can help SCI patients in neuro-rehabilitation training by directly translating patient motor imagery into walkers motion commands and thus bypassing spinal cord neurons completely. A non-invasive EEG based brain-computer interface is used for capturing patient neural activity. For signal processing and classification, an open source software (OpenVibe) is used. Classifiers categorize the patient motor imagery (MI) into a specific set of commands that are further translated into walker motion commands. The robotic walker also employs fall detection for ensuring safety of patient during gait training and can act as a support for SCI patients. The gait trainer is tested with subjects, and satisfactory results were achieved.

Keywords: brain computer interface (BCI), gait trainer, spinal cord injury (SCI), neurorehabilitation

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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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Authors: Thi Phuong Lan Nguyen

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Alongside technical skills, the employability is crucial for any graduates in the fast-evolving 21st century. It is reported that 78% of Australian students believe soft skills give advantages in the changing workforce due to technological automation (Oxford, 2020), which motivated to investigate how students whose English as a foreign or second language (EFL/ESL) are ready for the employability requirements in the new normal. Literature review, document analysis, and Interviews with EFL teachers are used in this research. The results of this research are helpful in preparing international EFL/ESL students to achieve the best preparation for currently increasing demanding employment markets, which are also meaningful for students themselves to be ready of being global citizens in the new normal.

Keywords: readiness, employability, EFL, ESL

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Authors: Georges F. Hatoum, Thomas H. Temple, Silvio Garcia, Xiaodong Wu

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Soft Tissue Sarcomas (STS) represent a diverse group of malignancies with heterogeneous clinical and pathological features. The treatment of extremity STS aims to achieve optimal local tumor control, improved survival, and preservation of limb function. The National Comprehensive Cancer Network guidelines, based on the cumulated clinical data, recommend radiation therapy (RT) in conjunction with limb-sparing surgery for large, high-grade STS measuring greater than 5 cm in size. Such treatment strategy can offer a cure for patients. However, when recurrence occurs (in nearly half of patients), the prognosis is poor, with a median survival of 12 to 15 months and with only palliative treatment options available. The spatially-fractionated-radiotherapy (SFRT), with a long history of treating bulky tumors as a non-mainstream technique, has gained new attention in recent years due to its unconventional therapeutic effects, such as bystander/abscopal effects. Combining single fraction of GRID, the original form of SFRT, with conventional RT was shown to have marginally increased the rate of pathological necrosis, which has been recognized to have a positive correlation to overall survival. In an effort to consistently increase the pathological necrosis rate over 90%, multiple fractions of Lattice RT (LRT), a newer form of 3D SFRT, interdigitated with the standard RT as neoadjuvant therapy was conducted in a preliminary clinical setting. With favorable results of over 95% of necrosis rate in a small cohort of patients, a Phase I/II clinical study was proposed to exam the safety and feasibility of this new strategy. Herein the design of the clinical study is presented. In this single-arm, two-stage phase I/II clinical trial, the primary objectives are >80% of the patients achieving >90% tumor necrosis and to evaluation the toxicity; the secondary objectives are to evaluate the local control, disease free survival and overall survival (OS), as well as the correlation between clinical response and the relevant biomarkers. The study plans to accrue patients over a span of two years. All patient will be treated with the new neoadjuvant RT regimen, in which one of every five fractions of conventional RT is replaced by a LRT fraction with vertices receiving dose ≥10Gy while keeping the tumor periphery at or close to 2 Gy per fraction. Surgical removal of the tumor is planned to occur 6 to 8 weeks following the completion of radiation therapy. The study will employ a Pocock-style early stopping boundary to ensure patient safety. The patients will be followed and monitored for a period of five years. Despite much effort, the rarity of the disease has resulted in limited novel therapeutic breakthroughs. Although a higher rate of treatment-induced tumor necrosis has been associated with improved OS, with the current techniques, only 20% of patients with large, high-grade tumors achieve a tumor necrosis rate exceeding 50%. If this new neoadjuvant strategy is proven effective, an appreciable improvement in clinical outcome without added toxicity can be anticipated. Due to the rarity of the disease, it is hoped that such study could be orchestrated in a multi-institutional setting.

Keywords: lattice RT, necrosis, SFRT, soft tissue sarcoma

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

Abstract:

Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Magdalena Smoktunowicz, Renata Wawrzyniak, Malgorzata Waleron, Krzysztof Waleron

Abstract:

Pectobacterium spp. were previously classified into the Erwinia genus founded in 1917 to unite at that time all Gram-negative, fermentative, nonsporulating and peritrichous flagellated plant pathogenic bacteria. After work of Waldee (1945), on Approved Lists of Bacterial Names and bacteriology manuals in 1980, they were described either under the species named Erwinia or Pectobacterium. The Pectobacterium genus was formally described in 1998 of 265 Pectobacterium strains. Currently, there are 21 species of Pectobacterium bacteria, including Pectobacterium betavasculorum since 2003, which caused soft rot on sugar beet tubers. Based on the biochemical experiments carried out for this, it is known that these bacteria are gram-negative, catalase-positive, oxidase-negative, facultatively anaerobic, using gelatin and causing symptoms of soft rot on potato and sugar beet tubers. The mere fact of growing on sugar beet may indicate a metabolism characteristic only for this species. Metabolomics, broadly defined as the biology of the metabolic systems, which allows to make comprehensive measurements of metabolites. Metabolomics, in combination with genomics, are complementary tools for the identification of metabolites and their reactions, and thus for the reconstruction of metabolic networks. The aim of this study was to apply the GC-MS-based untargeted metabolomics to study the metabolism of P. betavasculorum in different growing conditions. The metabolomic profiles of biomass and biomass media were determined. For sample preparation the following protocol was used: extraction with 900 µl of methanol: chloroform: water mixture (10: 3: 1, v: v) were added to 900 µl of biomass from the bottom of the tube and up to 900 µl of nutrient medium from the bacterial biomass. After centrifugation (13,000 x g, 15 min, 4oC), 300µL of the obtained supernatants were concentrated by rotary vacuum and evaporated to dryness. Afterwards, two-step derivatization procedure was performed before GC-MS analyses. The obtained results were subjected to statistical calculations with the use of both uni- and multivariate tests. The obtained results were evaluated using KEGG database, to asses which metabolic pathways are activated and which genes are responsible for it, during the metabolism of given substrates contained in the growing environment. The observed metabolic changes, combined with biochemical and physiological tests, may enable pathway discovery, regulatory inference and understanding of the homeostatic abilities of P. betavasculorum.

Keywords: GC-MS chromatograpfy, metabolomics, metabolism, pectobacterium strains, pectobacterium betavasculorum

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

Abstract:

This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale

Abstract:

It presents a heating system using low cost alternative solar collectors to promote the disinfection of water in low income communities that take water contaminated by bacteria. The system consists of two solar collectors, with total area of 4 m² and was built using PET bottles and cans of beer and soft drinks. Each collector is made up of 8 PVC tubes, connected in series and work in continuous flow. It will determine the flux the most appropriate to generate the temperature to promote the disinfection. Will be presented results of the efficiency and thermal loss of system and results of analysis of water after undergoing the process of heating.

Keywords: disinfection of water, solar heating system, poor communities, PVC

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

Abstract:

University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Bimrisha Mali

Abstract:

The present study investigates the type of errors English as a second language (ESL) learners produce using the definite article ‘the’. The participants were provided a questionnaire on the learner's ability test. The questionnaire consists of three cloze tests and two free composition tests. Each participant's response was received in the form of written data. A total of 78 participants from three government schools participated in the study. The participants are high-school students from Rural Assam. Assam is a north-eastern state of India. Their age ranged between 14-15. The medium of instruction and the communication among the students take place in the local language, i.e., Assamese. Pit Corder’s steps for conducting error analysis have been followed for the analysis procedure. Four types of errors were found (1) deletion of the definite article, (2) use of the definite article as modifiers as adjectives, (3) incorrect use of the definite article with singular proper nouns, (4) substitution of the definite article by the indefinite article ‘a’. Classifiers in Assamese that express definiteness is used with nouns, adjectives, and numerals. It is found that native language (L1) transfer plays a pivotal role in the learners’ errors. The analysis reveals the learners' inability to acquire the semantic connotation of definiteness in English due to native language (L1) interference.

Keywords: definite article error, l1 transfer, error analysis, ESL

Procedia PDF Downloads 110