Search results for: soft text classifier

Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Andriana Mkrtchyan, Vahe Khlghatyan

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The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

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Authors: Yung-Jin Weng, Chia-Chi Chang, Chun-Yu Tsai

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This paper is focused on the research of imprinting and replica molding of quasi-grey scale soft mold curved surface microstructure mold. In this paper, a magnetic photocuring forming system is first developed and built independently, then the magnetic curved surface microstructure soft mode is created; moreover, the magnetic performance of the magnetic curved surface at different heights is tested and recorded, and through experimentation and simulation, the magnetic curved surface microstructure soft mold is used in the research of quasi-grey scale soft mold curved surface microstructure imprinting and replica molding. The experimental results show that, under different surface curvatures and voltage control conditions, different quasi-grey scale array microstructures take shape. In addition, this paper conducts research on the imprinting and replica molding of photoresist composite magnetic powder in order to discuss the forming performance of magnetic photoresist, and finally, the experimental result is compared with the simulation to obtain more accurate prediction and results. This research is predicted to provide microstructure component preparation technology with heterogeneity and controllability, and is a kind of valid shaping quasi-grey scale microstructure manufacturing technology method.

Keywords: soft mold, magnetic, microstructure, curved surface

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Authors: Mario Kubek, Herwig Unger

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Centroid terms are single words that semantically and topically characterise text documents and so may serve as their very compact representation in automatic text processing. In the present paper, centroids are used to measure the relevance of text documents with respect to a given search query. Thus, a new graphbased paradigm for searching texts in large corpora is proposed and evaluated against keyword-based methods. The first, promising experimental results demonstrate the usefulness of the centroid-based search procedure. It is shown that especially the routing of search queries in interactive and decentralised search systems can be greatly improved by applying this approach. A detailed discussion on further fields of its application completes this contribution.

Keywords: search algorithm, centroid, query, keyword, co-occurrence, categorisation

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Bency Jacob, S.B. Waykar

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Degradations in historical document images appear due to aging of the documents. It is very difficult to understand and retrieve text from badly degraded documents as there is variation between the document foreground and background. Thresholding of such document images either result in broken characters or detection of false texts. Numerous algorithms exist that can separate text and background efficiently in the textual regions of the document; but portions of background are mistaken as text in areas that hardly contain any text. This paper presents a way to overcome these problems by a robust binarization technique that recovers the text from a severely degraded document images and thereby increases the accuracy of optical character recognition systems. The proposed document recovery algorithm efficiently removes degradations from document images. Here we are using the ostus method ,local thresholding and global thresholding and after the binarization training and recognizing the characters in the degraded documents.

Keywords: binarization, denoising, global thresholding, local thresholding, thresholding

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Authors: Hang T. T. Truong, Ronald S. Laura, Kylie Shaw

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Within Vietnam's system of higher education, its schools of business play a vital role in supporting the country’s economic objectives. However, the crucial contribution of soft skills for maximal success within the business sector has to date not been adequately recognized by its business schools. This being so, the development of the business school curriculum in Vietnam has not been able to 'catch up', so to say, with the burgeoning need of students for a comprehensive soft skills program designed to meet the national and global business objectives of their potential employers. The burden of the present paper is first to reveal the results of our survey in Vietnam which make explicit the extent to which major Vietnamese industrial employers’ value the potential role that soft skill competencies can play in maximizing business success. Our final task will be to determine which soft skills employers discern as best serving to maximize the economic interests of Vietnam within the global marketplace. Semi-structured telephone interviews have been conducted with the 15 representative Head Employers of Vietnam's reputedly largest and most successful of the diverse business enterprises across Vietnam. The findings of the study indicate that all respondents highly value the increasing importance of soft skills in business success. Our critical analysis of respondent data reveals that 19 essential soft skills are deemed by employers as integral to business workplace efficacy and should thus be integrated into the formal business curriculum. We are confident that our study represents the first comprehensive and specific survey yet undertaken within the business sector in Vietnam which accesses and analyses the opinions of representative employers from major companies across the country in regard to the growing importance of 19 specific soft skills essential for maximizing overall business success. Our research findings also reveal that the integration into business school curriculums nationwide of the soft skills we have identified is of paramount importance to advance the national and global economic interests of Vietnam.

Keywords: business curriculum, business graduates, employers’ perception, soft skills

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Authors: Suresh Reddi, Mathew Leslie, Vishnu S. Das

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Sustainable design is a prominent concept across all sectors of engineering and its importance is widely recognized within the Arabian Gulf region. Despite that sustainable or soft engineering options are not widely deployed in coastal engineering projects and a preference for utilizing ‘hard engineering’ solutions remain. The concept of soft engineering lies in “working together” with the nature to manage the coastline. This approach allows hard engineering options, such as breakwaters or sea walls, to be minimized or even eliminated altogether. Hard structures provide a firm barrier to wave energy or flooding, but in doing so they often have a significant impact on the natural processes of the coastline. This may affect the area locally or impact on neighboring zones. In addition, they often have a negative environmental impact and may create a sense of disconnect between the marine environment and local users. Soft engineering options, seek to protect the coastline by working in harmony with the natural process of sediment transport/budget. They often consider new habitat creation and creating usable spaces that will increase the sense of connection with nature. Often soft engineering options, where appropriately deployed can provide a low-maintenance, aesthetically valued, natural line of coastal protection. This paper deals with an overview of the following: The widely accepted soft engineering practices across the world; How this approach has been considered by Ramboll in some recent projects in Middle East and Asia; Challenges and barriers to use in using soft engineering options in the region; Way forward towards more widespread adoption.

Keywords: coastline, hard engineering, low maintenance, soft engineering options

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Authors: Kayode A. Olaniyi, Tola. M. Osifeko, Adeola A. Ogunleye

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Text line segmentation is an important step in document image processing. It represents a labeling process that assigns the same label using distance metric probability to spatially aligned units. Text line detection techniques have successfully been implemented mainly in printed documents. However, processing of the handwritten texts especially unconstrained documents has remained a key problem. This is because the unconstrained hand-written text lines are often not uniformly skewed. The spaces between text lines may not be obvious, complicated by the nature of handwriting and, overlapping ascenders and/or descenders of some characters. Hence, text lines detection and segmentation represents a leading challenge in handwritten document image processing. Text line detection methods that rely on the traditional global projection profile of the text document cannot efficiently confront with the problem of variable skew angles between different text lines. Hence, the formulation of a horizontal line as a separator is often not efficient. This paper presents a technique to segment a handwritten document into distinct lines of text. The proposed algorithm starts, by partitioning the initial text image into columns, across its width into chunks of about 5% each. At each vertical strip of 5%, the histogram of horizontal runs is projected. We have worked with the assumption that text appearing in a single strip is almost parallel to each other. The algorithm developed provides a sliding window through the first vertical strip on the left side of the page. It runs through to identify the new minimum corresponding to a valley in the projection profile. Each valley would represent the starting point of the orientation line and the ending point is the minimum point on the projection profile of the next vertical strip. The derived text-lines traverse around any obstructing handwritten vertical strips of connected component by associating it to either the line above or below. A decision of associating such connected component is made by the probability obtained from a distance metric decision. The technique outperforms the global projection profile for text line segmentation and it is robust to handle skewed documents and those with lines running into each other.

Keywords: connected-component, projection-profile, segmentation, text-line

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Authors: Man Li, Wanting Zhou, Lei Li

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Communication stability is the primary concern of communication satellites. Communication satellites are easily affected by particle radiation to generate single event effects (SEE), which leads to soft errors (SE) of the combinational logic circuit. The existing research on soft error rates (SER) of the combined logic circuit is mostly based on the assumption that the logic gates being bombarded have the same pulse width. However, in the actual radiation environment, the pulse widths of the logic gates being bombarded are different due to different linear energy transfers (LET). In order to improve the accuracy of SER evaluation model, this paper proposes a soft error rate evaluation method based on LET. In this paper, the authors analyze the influence of LET on the pulse width of combinational logic and establish the pulse width model based on the LET. Based on this model, the error rate of test circuit ISCAS'85 is calculated. The effectiveness of the model is proved by comparing it with previous experiments.

Keywords: communication satellite, pulse width, soft error rates, LET

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Authors: Trung Le Thanh

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Soil permeability coefficient is an important parameter that affects the effectiveness of mortar restoration work to reinforce soft soil. Currently, there are many methods to determine the permeability coefficient of ground through laboratory and field experiments. However, the value of the permeability coefficient is determined very differently depending on the geology in general and the sand base in particular. This article presents how to determine the permeability coefficient of sand foundation in Phu My Ward, Tan Uyen City, Binh Duong. The author analyzes and evaluates the advantages and disadvantages of assessment methods based on the data and results obtained, and on that basis recommends a suitable method for determining the permeability coefficient for sand foundations. The research results serve the evaluation of the effectiveness of grouting to reinforce soft ground in general, and grouting of bored piles in particular.

Keywords: permeability coefficient, soft soil, shaft grouting, post grouting, jet grouting

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Authors: Abdelhadi Nadjer

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The structure of the text to the systemic school -(glossématique-Helmslev). At the beginning of the note we have a cursory look around the concepts of general linguistics The science that studies scientific study of human language based on the description and preview the facts away from the trend of education than we gave a detailed overview the founder of systemic school and most important customers and more methods and curriculum theory and analysis they extend to all humanities, practical action each offset by a theoretical and the procedure can be analyzed through the elements that pose as another method we talked to its links with other language schools where they are based on the sharp criticism of the language before and deflected into consideration for the field of language and its erection has outside or language network and its participation in the actions (non-linguistic) and after that we started our Valglosamatik analytical structure of the text is ejected text terminal or all of the words to was put for expression. This text Negotiable divided into types in turn are divided into classes and class should not be carrying a contradiction and be inclusive. It is on the same materials as described relationships that combine language and seeks to describe their relations and identified.

Keywords: text, language schools, linguistics, human language

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Authors: Tina Ternes, Florian Kleinau

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The study investigates the content and triggers of mind wandering (MW) in readers of fictional texts. It asks whether readers’ MW is productive (text-related) or unproductive (text-unrelated). Methodologically, it bridges the gap between narratological and data-driven approaches by utilizing a sentence-by-sentence self-paced reading paradigm combined with thought probes in the reading of an excerpt of A. L. Kennedy’s “Baby Blue”. Results show that the contents of MW can be linked to text properties. We validated the role of self-reference in MW and found prediction errors to be triggers of MW. Results also indicate that the content of MW often travels along the lines of the text at hand and can thus be viewed as productive and integral to interpretation.

Keywords: narratology, mind wandering, reading fiction, meta cognition

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Authors: Quoc Hung Le Pham

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In the field of language study, classifier is an interesting research feature. In the world’s languages, some languages have classifier system, some do not. Mandarin Chinese and Vietnamese languages are a rich classifier system, however, because of the language system, the cognitive, cultural differences, so that the syntactic structure of classifier of them also dissimilar. When using Mandarin Chinese classifiers must collocate with nouns or verbs, in the lexical category it is not like nouns or verbs, belong to the open class. But some scholars believe that Mandarin Chinese measure words are similar to English and other Indo European languages. The word hanging on the structure and word formation (suffix), is a closed class. Compared to other languages, such as Chinese, Vietnamese, Thai and other Asian languages are still belonging to the classifier language’s second type, this type of language is classifier, it is in the majority of quantity must exist, and following deictic, anaphoric or quantity appearing together, not separation between its modified noun, also known as numeral classifier language. Main syntactic structure of Chinese classifiers are as follows: ‘quantity+measure+noun’, ‘pronoun+measure+noun’, ‘pronoun+quantity+measure+noun’, ‘prefix+quantity+measure +noun’, ‘quantity +adjective + measure +noun’, ‘ quantity (above 10 whole number), + duo (多)measure +noun’, ‘ quantity (around 10) + measure + duo (多) +noun’. Main syntactic structure of Vietnamese classifiers are: ‘quantity+measure+noun’, ‘ measure+noun+pronoun’, ‘quantity+measure+noun+pronoun’, ‘measure+noun+prefix+ quantity’, ‘quantity+measure+noun+adjective', ‘duo (多) +quanlity+measure+noun’, ‘quantity+measure+adjective+pronoun (quantity word could not be 1)’, ‘measure+adjective+pronoun’, ‘measure+pronoun’. In daily life, classifiers are commonly used, if Chinese learners failed to standardize this using catergory, because the negative impact might occur on their verbal communication. The richness of the Chinese classifier system contributes to the complexity in the study of the system by foreign learners, especially in the inter language of Vietnamese learners. As above mentioned, Vietnamese language also has a rich system of classifiers, however, the basic structure order of two languages are similar but both still have differences. These similarities and dissimilarities between Chinese and Vietnamese classifier systems contribute significantly to the common errors made by Vietnamese students while they acquire Chinese, which are distinct from the errors made by students from the other language background. This article from a comparative perspective of language, has an orientation towards Chinese and Vietnamese languages commonly used in classifiers semantics and structural form two aspects. This comparative study aims to identity Vietnamese students while learning Chinese classifiers may face some negative transference of mother language, beside that through the analysis of the classifiers questionnaire, find out the causes and patterns of the errors they made. As the preliminary analysis shows, Vietnamese students while learning Chinese classifiers made some errors such as: overuse classifier ‘ge’(个); misuse the other classifiers ‘*yi zhang ri ji’(yi pian ri ji), ‘*yi zuo fang zi’(yi jian fang zi), ‘*si zhang jin pai’(si mei jin pai); homonym words ‘dui, shuang, fu, tao’ (对、双、副、套), ‘ke, li’ (颗、粒).

Keywords: acquisition, classifiers, negative transfer, Vietnamse learners

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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: Julita D. L. Nainggolan, Cindy Novita Ongkowijoyo, Veli Sungono, Dyana Safitri Velies, Ernestine Vivie Sadeli, Jimmy

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Introduction: Normal menstrual cycle in women ranges from 21-34 days. Menopause is defined as the time when there have been no menstrual periods for 12 consecutive months and no other biological or physiological cause can be identified. Caffeine might increase the estradiol in the early of follicular phase and possibly increase the progesterone and shorten menstruation cycle. Women with shorter menstrual cycle, (below 26 days) would likely get to menopause 1.4 years earlier than those who are normal, and 2.2 years earlier than women with longer menstrual cycle. Purpose: To study the association of caffeine consumption in coffee, tea, and soft drinks with the age of menopause. Design Study: A cross-sectional study using purposive sampling of 132 menopause women from elderly nursing, hospitals and students’ relatives from August 2015-December 2015. The mean difference of age of menopause among the caffeine intake was analyzed by using the unpaired t-test and logistic regression. Results: Mean current age of the respondents are 61.4 years ± SD 9.8; and age of menopause was 47.7 years ± SD 4.2. There are 49.6% who drink coffee, 62.6% of tea and 7.6% of soft drinks. The analysis of t-test showed no significant mean difference in age of menopause among women who drink coffee, tea and soft drinks, mean age of 47.63 ± 4.3 in coffee with p=0.392, mean age of 47.8 ± 4 in tea with p=0.373; and mean age of 46 ± 5.5 with p=0.083 after adjustment of smoking history. Conclusion: Consumption of caffeine among women who drink coffee, tea, and soft drinks did not show significant mean difference in age of menopause.

Keywords: caffeine, menopause, coffee, tea, soda, soft drinks

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Petr Sifta, Vaclav Bittner, Martin Kysela, Matej Kolar

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The purpose of the research described in this work is to answer how to measure the rheologic (viscoelastic) properties tendo–deformational characteristics of soft tissue. The method would also resemble muscle palpation examination as it is known in clinical practice. For this purpose, an instrument with the working name “myotonometer” has been used. At present, there is lack of objective methods for assessing the muscle tone by viscous and elastic properties of soft tissue. That is why we decided to focus on creating or finding quantitative and qualitative methodology capable of specifying muscle tone.

Keywords: rheologic properties, tendo–deformational characteristics, viscosity, elasticity, hypertonus

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Authors: Yaser Kazemzadeh, Keyvan Molanoruzy, Mojtaba Izady

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The balance is the key component of motor skills to maintain postural control and the execution of complex skills. The present study was designed to evaluate the impact of soft knee pads on static and dynamic balance of male athletes. For this aim, thirty young athletes in different sport fields with 3 years professional sport training background and thirty healthy young men nonathletic (age: 24.5 ± 2.9, 24.3 ± 2.4, weight: 77.2 ± 4.3 and 80/9 ± 6/3 and height: 175 ± 2/84, 172 ± 5/44 respectively) as subjects selected. Then, subjects in two manner (without knee and with soft knee pads made of neoprene) execute standard error test (BESS) to assess static balance and star test to assess dynamic balance. For analyze of data, t-tests and one-way ANOVA were significant 05/0 ≥ α statistical analysis. The results showed that the use of soft knee significantly reduced error rate in static balance test (p ≥ 0/05). Also, use a soft knee pads decreased score of athlete group and increased score of nonathletic group in star test (p ≥ 0/05). These findings, indicates that use of knees affects static and dynamic balance in athletes and nonathletic in different manner and may increased athletic performance in sports that rely on static balance and decreased performance in sports that rely on dynamic balance.

Keywords: static balance, dynamic balance, soft knee, athletic men, non athletic men

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Xiuqin Ma, Hongwu Qin

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A new efficient normal parameter reduction algorithm of soft set in decision making was proposed. However, up to the present, few documents have focused on real-life applications of this algorithm. Accordingly, we apply a New Efficient Normal Parameter Reduction algorithm into real-life datasets of online shopping, such as Blackberry Mobile Phone Dataset. Experimental results show that this algorithm is not only suitable but feasible for dealing with the online shopping.

Keywords: soft sets, parameter reduction, normal parameter reduction, online shopping

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Authors: Awadhesh Kishore

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One of the most unique characteristics of rabbit feeding behaviour is caecotrophy, which involves the excretion and immediate consumption of specific faeces known as soft faeces. Caecotrophy in rabbits is the instinctual behaviour of eating soft faeces; reduced caecotrophy decreases rabbit growth and lipid synthesis in the liver. Caecotroph ingestion is highest when rabbits are fed a diet high in indigestible fibre. The colon produces two types of waste: hard and soft pellets. The hard pellets are expelled, but the soft pellets are re-ingested by the rabbit directly upon being expelled from the anus by twisting itself around and sucking in those pellets as they emerge from the anus. The type of alfalfa hay in the feed of the rabbits does not affect volatile fatty acid concentration, the pattern of fermentation, or pH in the faeces. The cecal content and the soft faeces contain significant amounts of retinoids and carotenoids, while in the tissues (blood, liver, and kidney), these pigments do not occur in substantial amounts. Preventing caecotrophy reduced growth and altered lipid metabolism, depressing the development of new approaches for rabbit feeding and production. Relative abundance is depressed for genes related to metabolic pathways such as vitamin C and sugar metabolism, vitamin B2 metabolism, and bile secretion. The key microorganisms that regulate the rapid growth performance of rabbits may provide useful references for future research and the development of microecological preparations.

Keywords: caecocolonic microorganisms, caecotrophy, fasting caecotrophy, rabbits, soft pellets

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Authors: P. Zackiewicz, M. Hreczka, R. Kolano, A. Kolano-Burian

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New types of materials applied as the stators in the Permanent Magnet Brushless Direct Current motors used in the heart supporting pumps are presented. The main focus of this work is the research on the fabrication of a hybrid nine-pole soft magnetic core consisting of a soft magnetic carrier ring with rectangular notches, made from the FeSi3 strip, and nine soft magnetic poles. This soft magnetic core is made in three stages: (a) preparation of the carrier rings from soft magnetic material with the lowest possible power losses and suitable stiffness, (b) preparation of trapezoidal soft magnetic poles from Metglas 2605 SA1 type ribbons, and (c) making durable connection between the poles and the carrier ring, capable of withstanding a four-times greater tearing force than that present during normal operation of the motor pump. All magnetic properties measurements were made using Remacomp C-1200 (Magnet Physik, Germany) and 450 Gaussometer (Lake Shore, USA) and the electrical characteristics were measured using laboratory generator DF1723009TC (NDN, Poland). Specific measurement techniques used to determine properties of the hybrid cores were presented. Obtained results allow developing the fabrication technology with an account of the intended application of these cores in the stators of the low-power PMBLDC motors used in implanted heart operation supporting pumps. The proposed measurement methodology is appropriate for assessing the quality of the stators.

Keywords: amorphous materials, heart supporting pump, PMBLDC motor, soft magnetic materials

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Authors: Nor Diana Ahmad, Eric Atwell, Brandon Bennett

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Text processing techniques for English have been developed for several decades. But for the Malay language, text processing methods are still far behind. Moreover, there are limited resources, tools for computational linguistic analysis available for the Malay language. Therefore, this research presents the use of natural language processing (NLP) in processing Malay translated Qur’an text. As the result, a new language resource for Malay translated Qur’an was created. This resource will help other researchers to build the necessary processing tools for the Malay language. This research also develops a simple question-answer prototype to demonstrate the use of the Malay Qur’an resource for text processing. This prototype has been developed using Python. The prototype pre-processes the Malay Qur’an and an input query using a stemming algorithm and then searches for occurrences of the query word stem. The result produced shows improved matching likelihood between user query and its answer. A POS-tagging algorithm has also been produced. The stemming and tagging algorithms can be used as tools for research related to other Malay texts and can be used to support applications such as information retrieval, question answering systems, ontology-based search and other text analysis tasks.

Keywords: language resource, Malay translated Qur'an, natural language processing (NLP), text processing

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Authors: Victor N. Anisimov, Lyubov A. Boyko, Yazgul R. Almukhametova, Natalia V. Galkina, Alexander V. Latanov

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Modern commercials presented on billboards, TV and on the Internet contain a lot of information about the product or service in text form. However, this information cannot always be perceived and understood by consumers. Typical sociological focus group studies often cannot reveal important features of the interpretation and understanding information that has been read in text messages. In addition, there is no reliable method to determine the degree of understanding of the information contained in a text. Only the fact of viewing a text does not mean that consumer has perceived and understood the meaning of this text. At the same time, the tools based on marketing analysis allow only to indirectly estimate the process of reading and understanding a text. Therefore, the aim of this work is to develop a valid method of recording objective indicators in real time for assessing the fact of reading and the degree of text comprehension. Psychophysiological parameters recorded during text reading can form the basis for this objective method. We studied the relationship between multimodal psychophysiological parameters and the process of text comprehension during reading using the method of correlation analysis. We used eye-tracking technology to record eye movements parameters to estimate visual attention, electroencephalography (EEG) to assess cognitive load and polygraphic indicators (skin-galvanic reaction, SGR) that reflect the emotional state of the respondent during text reading. We revealed reliable interrelations between perceiving the information and the dynamics of psychophysiological parameters during reading the text in commercials. Eye movement parameters reflected the difficulties arising in respondents during perceiving ambiguous parts of text. EEG dynamics in rate of alpha band were related with cumulative effect of cognitive load. SGR dynamics were related with emotional state of the respondent and with the meaning of text and type of commercial. EEG and polygraph parameters together also reflected the mental difficulties of respondents in understanding text and showed significant differences in cases of low and high text comprehension. We also revealed differences in psychophysiological parameters for different type of commercials (static vs. video, financial vs. cinema vs. pharmaceutics vs. mobile communication, etc.). Conclusions: Our methodology allows to perform multimodal evaluation of text perusal and the quality of text reading in commercials. In general, our results indicate the possibility of designing an integral model to estimate the comprehension of reading the commercial text in percent scale based on all noticed markers.

Keywords: reading, commercials, eye movements, EEG, polygraphic indicators

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Authors: Nabil A. Ahmed

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DC-DC converters are necessary to interface low-voltage fuel cell power generation systems to a higher voltage DC bus system. A system and method for generating a regulated output power from fuel cell power generation systems is proposed in this paper, this includes a soft-switching isolated DC-DC converter to reduce the idling and circulating currents. The system incorporates a high-frequency center tap transformer link DC-DC converter using secondary-side soft switching control. Snubber capacitors including the parasitic capacitance of the switching devices and the transformer leakage inductance are utilized to achieve zero-voltage switching (ZVS) in the primary side of the high-frequency transformer. Therefore, no extra resonant components are required for ZVS. The inherent soft-switching capability allows high power density, efficient power conversion, and compact packaging. A prototype rated at 6.5 kW is proposed and simulated. Simulation results confirmed a wide range of soft-switching operation and consequently high conversion efficiency will be achieved.

Keywords: secondary-side, phase-shift, high-frequency transformer, zero voltage, zero current, soft switching operation, switching losses

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Authors: Rosa Figueroa, Christopher Flores

Abstract:

Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: Zayar Phyo, Ei Chaw Htoon

Abstract:

In a New-LSB based Steganography method, the Fisher-Yates algorithm is used to permute an existing array randomly. However, that algorithm performance became slower and occurred memory overflow problem while processing the large dimension of images. Therefore, the Text-Based Shuffling algorithm aimed to select only necessary pixels as hiding characters at the specific position of an image according to the length of the input text. In this paper, the enhanced text-based shuffling algorithm is presented with the powered of GPU to improve more excellent performance. The proposed algorithm employs the OpenCL Aparapi framework, along with XORShift Kernel including the Pseudo-Random Number Generator (PRNG) Kernel. PRNG is applied to produce random numbers inside the kernel of OpenCL. The experiment of the proposed algorithm is carried out by practicing GPU that it can perform faster-processing speed and better efficiency without getting the disruption of unnecessary operating system tasks.

Keywords: LSB based steganography, Fisher-Yates algorithm, text-based shuffling algorithm, OpenCL, XORShiftKernel

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Authors: Sidi Yang, Haiyi Zhang

Abstract:

Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: text mining, Twitter, topic model, sentiment analysis

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Authors: Beier Zhu, Rui Zhang, Qi Song

Abstract:

Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

Procedia PDF Downloads 165