Search results for: skills gained through learning

Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Save Kumwenda, Khumbo Kalulu, Kondwani Chidziwisano, Limbani Kalumbi, Vincent Doyle, Bagrey Ngwira

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Introduction: The Malawi Government in partnership with Non-Governmental Organizations adopted Community Led Total Sanitation (CLTS) in 2008 as an approach in sanitation and hygiene promotion with an aim of declaring Malawi Open Defeacation Free (ODF) by 2015. While there is a significant body of research into CLTS available in public domain, there is little research done on challenges faced in implementing CLTS in Malawi. Methods: A cross-sectional qualitative study was carried out in three districts of Ntcheu, Balaka, and Phalombe. Data was collected using Focus Group Discussions (FGDs) and Key informant interviews (KII) and analysed manually. Results: In total, 96 people took part in FGDs and 9 people in KII. It was shown that choice of leaders after triggering was commonly done by chiefs, facilitators, and VHC without following CLTS principles as opposed to identifying individuals who showed leadership skills. Despite capacity building initiatives involving District Coordinating Teams, lack of resources to undertake follow-ups contributed to failure to sustain ODF in the community. It was also found that while most respondents appreciating the need for no subsidies, the elderly and those with disabilities felt the need for external support because do not have money for buying strong logs, slabs for durable toilet floor and also to hire people to build latrines for them. Conclusion: Effective implementation of CLTS requires comprehensive consideration of various issues that may affect its success.

Keywords: open defecation, community-led, sanitation, faecal matter, hygiene, Malawi

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Bara Yousef

Abstract:

The development of independency and functional participation is an important therapeutic goal for many children with cerebral palsy,They was many therapeutic approach have been used for treatment those children like neurodevelopment treatment, balance training strengthening and stretching exercise. More recently, therapy for children with cerebral palsy has focused on achieving functional goals using task-oriented interventions and summer camping model, which focus on activities that relevant and meaningful to the child, to learn more efficient and effective motor skills. We explore the effectiveness of using functional rehabilitation comparing with regular rehabilitation among 40 Saudi children with cerebral palsy in pediatric unit at Sultan Bin Abdul Aziz Humanitarian City-Ksa ,where 20 children randomly assign in control group who received rehabilitation based on regular therapy approach and other 20 children assign on experiment group who received rehabilitation based on functional therapy approach with an average of 45min OT treatment and 45 min PT treatment- daily within a period of 6 week. Our finding reported that children in experiment group has improved in gross motor function with an average from 49.4 to 57.6 based on GMFM 66 as primary outcome measure and improved in WeeFIM with an average from 52 to 62 while children in control group has improved with an average from 48.4 to 53.7 in GMFM and from 53 to and 58 in WeeFIM. Consequently, there has been growing interest in determining the effects of functional training programs as promising approach for these children.

Keywords: Cerebral Palsy (CP), gross motor function measure (GMFM66), pediatric Functional Independent Measure (WeeFIM), rehabilitation, disability

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Authors: Rayah Asiri, Anna Robinson-Barella, Adam Todd, Andy Husband

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Introduction: A high prevalence of diabetes and diabetes-related complications in ethnic minority communities is of significant concern. Several studies have indicated low adherence rates to antidiabetic medications in ethnic minorities. Poor adherence to antidiabetic medications leads to a higher risk of complications and mortality. This review aims to explore the barriers to and facilitators of adherence to antidiabetic medication among ethnic minority groups in high-income countries. Methods: A comprehensive search of MEDLINE, Embase, CINAHL, and PsycINFO databases for qualitative studies exploring the barriers to or facilitators of adherence to antidiabetic medication in ethnic minority groups were conducted from database inception to March 2022 (PROSPERO CRD42022320681). A quality assessment of the studies was conducted using the Critical Appraisal Skills Programme (CASP) tool. Key concepts and themes from relevant studies were synthesised using a meta-ethnographic approach. Result: A total of 18 studies were included in the review, and three major themes were developed: 1) cultural underpinnings, 2) communication and building relationships, and 3) managing diabetes during holidays. Conclusion: Multiple barriers and facilitators of adherence to antidiabetic medication among ethnic minority people in high-income countries have been identified. A medication adherence intervention focusing on identified barriers to adherence to antidiabetic medication in ethnic minorities may help in improving diabetes outcomes in these groups.

Keywords: medication adherence, diabetes, ethnic minority, barriers, facilitators

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Authors: Godaye Gobena Gomiole

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The purpose of this study was to investigate school context-related factors that affect principals' instructional leadership competence in primary schools in Tarcha Town, Ethiopia. A qualitative case study research design was used. The data were collected through semi-structured interviews and document analysis. Twelve senior principals were included in the study through purposive sampling. Interviews were used to collect in-depth data. The data was analyzed thematically. The findings of the study indicated that primary school principals face both internal and external challenges. Internally, they face limited knowledge and skills, a lack of courage and commitment, and administrative work overload. Their external challenges included negative attitudes from parents and teachers, a lack of instructional materials, and little support from local education authorities. Consequently, they can't serve effectively as instructional leaders or resource people. Based on the findings, it is recommended that the Ministry of Education, South West Regional Education Bureau, Dawuro Zone Education Department, and Tarcha Town Administration Education Officers and Cluster Supervisors regularly monitor and support school leaders and prepare and provide pertinent teaching materials and training so that the principals can lead in the capacity that is appropriate for the position.

Keywords: instructional leadership, primary school, principals, school context related factors

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Authors: G. Erkomaishvili, M. Kobalava, T. Lazariashvili, N. Damenia

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Current state of foreign direct investment (FDI) in Georgia is analyzed and evaluated in the paper, the existing legislative background for regulating investments and stimulating policies to attract investments are shown. It is noted that in developing countries encouragement of investment activity, support and implementation are of the most important tasks, implying a consistent investment policy, investor-friendly tax regime and the legal system, reducing administrative barriers and restrictions, fare competitive conditions and business development infrastructure. The work deals with the determining factor of FDIs and the main directions of stimulation, as well as prospective industries where new investments are needed. Contributing and hindering factors and stimulating measures are analyzed. As a result of the research, the direct and indirect factors attracting FDI have been identified. Facilitating factors to FDI inflow are as follows: simplicity of starting business, geopolitical location, low taxes, access to credit, ease of ownership registration, natural resources, low burden of regulations, low level of corruption and low crime rates. Hindering factors to FDI inflow are as follows: small market, lack of policy for attracting investments, low qualification of the workforce (despite the large number of unemployed people it is difficult to find workers with necessary special skills and qualifications), high interest rates, instability of national currency exchange rate, presence of conflict zones within the country and so forth.

Keywords: foreign direct investment, investor, investment attracting marketing policies, reinvestment

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Authors: Zasha Romero, Joe Paschall

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This poster will detail the importance of physical activity for the Visually Impaired students and how to promote inclusion in fitness through way of social gatherings and jogging. Furthermore, it will demonstrate how a Health & Kinesiology University Club cooperated in the journey of visually impaired students from participating in physical activity to completing their first 10K fun run. Purpose: The poster will detail how a university’s Health & Kinesiology Club developed a program to promote participation in fitness activities for visually impaired individuals. Also, it will detail their journey from participation in physical activity to completing a 10K fun run. Methods: In an effort to promote inclusion of all into physical activity, a university’s Health & Kinesiology Club developed a non-profit program to challenge visually impaired students to train and complete a 10 kilometer fun run in a South Texas town. The idea was to promote physical fitness through way of social interaction. In order to maintain runners interested, Club students developed training plans and strategies to be able to navigate in a race that was attended by over 18,000 runners. The idea was to promote interaction and life-long fitness amongst participants. Implications: This strategy was done in collaboration with different non-profit institutions to create awareness and provide opportunities for physical fitness, social interaction and life-long fitness skills associated with the jogging. The workshop provided collaboration amongst different entities and novel ideas to create opportunities for a typically underserved population.

Keywords: inclusion, participation, management, disability, fitness

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Authors: Deepa Idnani

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Attendance is an important factor in school success among children. Studies show that better attendance is related to higher academic achievement for students of all backgrounds, but particularly for children with lower socio-economic status. Beginning from the early years, students who attend school regularly score higher on tests than their peers who are frequently absent. The present study in different types of School In Delhi tries to highlight the impact of student absenteeism and the challenges it poses for the students. The study relies on Lewin ‘Model of Exclusion’ and tries to focus on the analysis of children with special needs and the inclusion and exclusion of students in the school.

Keywords: student absenteeism, pedagogy, learning, right to education act, exclusion

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Somayeh Sharifi

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The purpose of this study is to investigate the effects of collaborative reflection and class observation on improving the quality of teacher training courses and the students reading comprehension skills. 13 inexperienced English teachers teaching elementary courses that were at the same level of proficiency were chosen. Thirteen participants were allocated in two groups, with 7 teachers in the experimental group and the other 6 teachers in the control group. Since two groups were not selected randomly, this study is a form of quasi-experimental research. In addition to a 3-day teacher training course for both groups, teachers in experimental group recorded and observed 20 sessions of their own classes and 30 sessions of experienced teachers’ class and participated in 12 meetings -3 month once a week- in which teachers shared any event that they found interesting during observations and their own teaching and compare it with strategies that they learned in teacher training courses. In contrast, the control group did not engage in any process of observation and collaboration. In order to test students' performance in English before and after the treatment, a Key English Test (KET) was employed to check students' reading skill. The result of the test shows that there is not a significant difference in mean of scores in KET pretest in and, since they are close to each other. However by considering mean and median of posttest in both classes we perceive that although both control and experimental group students' proficiency in English enhanced, there was a significant difference in experimental group students' final scores before and after treatment.

Keywords: collaborative reflection, reading comprehension, teacher training courses, key English test (KET)

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: text detection, CNN, PZM, deep learning

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Authors: Kazuhisa Takagi

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This paper is about movies and dynamic objects for mobile phones. Dynamic objects are the software programmed by JavaScript. They consist of geometric figures and work on HTML5-compliant browsers. Mobile phones are very popular among teenagers. They like watching movies and playing games on them. So, mathematics movies and dynamic objects would enhance teaching and learning processes. In the movies, manga characters speak with artificially synchronized voices. They teach trigonometry together with dynamic mathematical objects. Many movies are created. They are Windows Media files or MP4 movies. These movies and dynamic objects are not only used in the classroom but also distributed to students. By watching movies, students can study trigonometry before or after class.

Keywords: dynamic mathematical object, javascript, google drive, transfer jet

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Authors: Faustina Masocha, Vusani Moyo

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For decades, dividend announcements have been presumed to contain important signals about the future prospects of companies. Similarly, the same has been presumed about management earnings announcements. Despite both dividend and earnings announcements being considered informative, a number of researchers questioned their credibility and found both to contain short-term signals. Pertaining to dividend announcements, some authors argued that although they might contain important information that can result in changes in share prices, which consequently results in the accumulation of abnormal returns, their degree of informativeness is less compared to other signaling tools such as earnings announcements. Yet, this claim in favor has been refuted by other researchers who found the effect of earnings to be transitory and of little value to shareholders as indicated by the little abnormal returns earned during the period surrounding earnings announcements. Considering the above, it is apparent that both dividends and earnings have been hypothesized to have a signaling impact. This prompts one to question which between these two signaling tools is more informative. To answer this question, two follow-up questions were asked. The first question sought to determine the event which results in the most effect on share prices, while the second question focused on the event that influenced trading volume the most. To answer the first question and evaluate the effect that each of these events had on share prices, an event study methodology was employed on a sample made up of the top 10 JSE-listed companies for data collected from 2012 to 2019 to determine if shareholders gained abnormal returns (ARs) during announcement dates. The event that resulted in the most persistent and highest amount of ARs was considered to be more informative. Looking at the second follow-up question, an investigation was conducted to determine if either dividends or earnings announcements influenced trading patterns, resulting in abnormal trading volumes (ATV) around announcement time. The event that resulted in the most ATV was considered more informative. Using an estimation period of 20 days and an event window of 21 days, and hypothesis testing, it was found that announcements pertaining to the increase of earnings resulted in the most ARs, Cumulative Abnormal Returns (CARs) and had a lasting effect in comparison to dividend announcements whose effect lasted until day +3. This solidifies some empirical arguments that the signaling effect of dividends has become diminishing. It was also found that when reported earnings declined in comparison to the previous period, there was an increase in trading volume, resulting in ATV. Although dividend announcements did result in abnormal returns, they were lesser than those acquired during earnings announcements which refutes a number of theoretical and empirical arguments that found dividends to be more informative than earnings announcements.

Keywords: dividend signaling, event study methodology, information content of earnings, signaling theory

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Authors: Tomasz Łęga, Marta Sosnowska, Mirosława Panasiuk, Lilit Hovhannisyan, Beata Gromadzka, Marcin Olszewski, Sabina Zoledowska, Dawid Nidzworski

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Toxic heavy metal ion contamination of industrial wastewater has recently become a significant environmental concern in many regions of the world. Although the majority of heavy metals are naturally occurring elements found on the earth's surface, anthropogenic activities such as mining and smelting, industrial production, and agricultural use of metals and metal-containing compounds are responsible for the majority of environmental contamination and human exposure. The permissible limits (ppm) for heavy metals in food, water and soil are frequently exceeded and considered hazardous to humans, other organisms, and the environment as a whole. Human exposure to highly nickel-polluted environments causes a variety of pathologic effects. In 2008, nickel received the shameful name of “Allergen of the Year” (GILLETTE 2008). According to the dermatologist, the frequency of nickel allergy is still growing, and it can’t be explained only by fashionable piercing and nickel devices used in medicine (like coronary stents and endoprostheses). Effective remediation methods for removing heavy metal ions from soil and water are becoming increasingly important. Among others, methods such as chemical precipitation, micro- and nanofiltration, membrane separation, conventional coagulation, electrodialysis, ion exchange, reverse and forward osmosis, photocatalysis and polymer or carbon nanocomposite absorbents have all been investigated so far. The importance of environmentally sustainable industrial production processes and the conservation of dwindling natural resources has highlighted the need for affordable, innovative biosorptive materials capable of recovering specific chemical elements from dilute aqueous solutions. The use of combinatorial phage display techniques for selecting and recognizing material-binding peptides with a selective affinity for any target, particularly inorganic materials, has gained considerable interest in the development of advanced bio- or nano-materials. However, due to the limitations of phage display libraries and the biopanning process, the accuracy of molecular recognition for inorganic materials remains a challenge. This study presents the isolation, identification and characterisation of metal binding phage clones that preferentially recover nickel.

Keywords: Heavy metal recovery, cleaning water, phage display, nickel

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Authors: Amir Ahmadi, Hossein Ahmadi, Alireza Ahmadi

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Emotional Intelligence (EI) has been defined as the ability to empathize, persevere, control impulses, communicate clearly, make thoughtful decisions, solve problems, and work with others in a way that earns friends and success. These abilities allow an individual to recognize and regulate emotion, develop self-control, set goals, develop empathy, resolve conflicts, and develop skills needed for leadership and effective group participation. Due to the increasing complexity of organizations and different ways of thinking, attitudes and beliefs of individuals, Conflict as an important part of organizational life has been examined frequently. The main point is that the conflict is not necessarily in organization, unnecessary; But it can be more creative (increase creativity), to promote innovation, or may avoid wasting energy and resources of the organization. The purpose of this study was to investigate the relation between principals emotional intelligence as one of the factors affecting conflict management among teachers. This relation was analyzed through cluster sampling with a sample size consisting of 120 individuals. The results of the study showed that, at the 95% level of confidence, the two secondary hypotheses (i.e. relation between emotional intelligence of principals and use of competition and cooperation strategies of conflict management among teachers)were confirmed, but the other three secondary hypotheses (i.e. the relation between emotional intelligence of managers and use of avoidance, adaptation and adaptability strategies of conflict management among teachers) were rejected. The primary hypothesis (i.e. relation between emotional intelligence of principals with conflict management among teachers) is supported.

Keywords: emotional intelligence, conflict, conflict management, strategies of conflict management

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Authors: Maria Korman, Carmit Gal, Ella Gabitov, Avi Karni

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The main question addressed in this study was whether the time-of-day wherein training is afforded is a significant factor for motor skill ('how-to', procedural knowledge) acquisition and consolidation into long term memory in the healthy elderly population. Twenty-nine older adults (60-75 years) practiced an explicitly instructed 5-element key-press sequence by repeatedly generating the sequence ‘as fast and accurately as possible’. Contribution of three parameters to acquisition, 24h post-training consolidation, and 1-week retention gains in motor sequence speed was assessed: (a) time of training (morning vs. evening group) (b) sleep quality (actigraphy) and (c) chronotype. All study participants were moderately morning type, according to the Morningness-Eveningness Questionnaire score. All participants had sleep patterns typical of age, with average sleep efficiency of ~ 82%, and approximately 6 hours of sleep. Speed of motor sequence performance in both groups improved to a similar extent during training session. Nevertheless, evening group expressed small but significant overnight consolidation phase gains, while morning group showed only maintenance of performance level attained at the end of training. By 1-week retention test, both groups showed similar performance levels with no significant gains or losses with respect to 24h test. Changes in the tapping patterns at 24h and 1-week post-training were assessed based on normalized Pearson correlation coefficients using the Fisher’s z-transformation in reference to the tapping pattern attained at the end of the training. Significant differences between the groups were found: the evening group showed larger changes in tapping patterns across the consolidation and retention windows. Our results show that morning-oriented older adults effectively acquired, consolidated, and maintained a new sequence of finger movements, following both morning and evening practice sessions. However, time-of-training affected the time-course of skill evolution in terms of performance speed, as well as the re-organization of tapping patterns during the consolidation period. These results are in line with the notion that motor training preceding a sleep interval may be beneficial for the long-term memory in the elderly. Evening training should be considered an appropriate time window for motor skill learning in older adults, even in individuals with morning chronotype.

Keywords: time-of-day, elderly, motor learning, memory consolidation, chronotype

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: Evangelia-Chrysanthi Kouklari, Stella Tsermentseli, Claire P. Monks

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Executive function (EF) refers to a set of future-oriented and goal-directed cognitive skills that are crucial for problem solving and social behaviour, as well as the ability to organise oneself. It has been suggested that EF could be conceptualised as two distinct but interrelated constructs, one emotional (hot) and one cognitive (cool), as it facilitates both affective and cognitive regulation. Cool EF has been found to be strongly related to Theory of Mind (ToM) that is the ability to infer mental states, but research has not taken into account the association between hot EF and ToM in Autism Spectrum Disorder (ASD) to date. The present study investigates the associations between both hot and cool EF and ToM in school-aged children with ASD. This cross-sectional study assesses 79 school-aged children with ASD (7-15 years) and 91 controls matched for age and IQ, on tasks tapping cool EF (working memory, inhibition, planning), hot EF (effective decision making, delay discounting), and ToM (emotional understanding and false/no false belief). Significant group differences in each EF measure support a global executive dysfunction in ASD. Strong associations between hot EF and ToM in ASD are reported for the first time (i.e. ToM emotional understanding and delay discounting). These findings highlight that hot EF also makes a unique contribution to the developmental profile of ASD. Considering the role of both hot and cool EF in association with ToM in individuals with ASD may aid in gaining a greater understanding not just of how these complex multifaceted cognitive abilities relate to one another, but their joint role in the distinct developmental pathway followed in ASD.

Keywords: ASD, executive function, school age, theory of mind

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Authors: Sema Müge Özdemiray

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In line with the new public management approach to provide effective and efficient services necessary to achieve the social goals of public institutions, employees must have the knowledge and skills required by the age. In conjunction with the transition from personnel management to human resources management, it is seen that there is a change in the understanding of in-service training, the understanding of "required in-service training" has switched to the understanding of "continuous in-service training". However, in terms of in-service training in Turkey, it seems to be trouble at the point of adopting to change. The main purpose of this study is to primarily create a conceptual framework of in-service training and subsequently determine, analyze and discuss the developments and problems faced by in-service training in Turkey in the transition from personnel management to human resources management. In accordance with this purpose, the necessary data of this study were collected using qualitative approaches. Observation and document analysis was used and content analysis was performed on the data gathered in the study. The results of this study, according to data such as the number of institutions requesting in-service training, allocated budget of in-service training, the number of people participating in such training, transition of personnel management to human resources management should not lead to a paradigm shift in Turkey’s understanding of in-service training, although this is compulsory for public institutions in accordance with the law in Turkey. In-service training in Turkish public administration is still not implemented effectively and is seen as a social activity for employees and a formality for institutions.

Keywords: Human resources management, in service training, personnel management, public institutions

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Authors: Ava Zarif Sanayei, Sedigheh Sina

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Radiological protection for plants and animals is an area of regulatory importance. Acute doses of 0.1 Gy/d (10 rad/d) or below are highly unlikely to produce permanent, measurable negative effects on populations or communities of plants or animals. The advancement of radio diagnostics for domestic animals, particularly dogs and cats, has gained popularity in veterinary medicine. As pets are considered to be members of the family worldwide, they are entitled to the same care and protection. It is important to have a system of radiological protection for nonhuman organisms that complies with the focus on human health as outlined in ICRP publication 19. The present study attempts to assess surface-skin entrance doses in small pets undergoing abdominal radio diagnostic procedures utilizing a direct measurements technique with a thermoluminescent dosimeter. These measurements allow the determination of the entrance skin dose (ESD) by calculating the amount of radiation absorbed by the skin during exposure. A group of Thirty TLD-100 dosimeters produced by Harshaw Company, each with a repeatability greater than 95% and calibration using ¹³⁷Cs gamma source, were utilized to measure doses to ten small pets, including cats and dogs in the radiological department in a veterinary clinic in Shiraz, Iran. Radiological procedures were performed using a portable imaging unit (Philips Super M100, Philips Medical System, Germany) to acquire images of the abdomen; ten exams of abdomen images of different pets were monitored, measuring the thicknesses of the two projections (lateral and ventrodorsal) and the distance of the X-ray source from the surface of each pet during the exams. A group of two dosimeters was used for each pet which has been stacked on their skin on the abdomen region. The outcome of this study involved medical procedures with the same kVp, mAs, and nearly identical positions for different diagnostic X-ray procedures executed over a period of two months. The result showed the mean ESD value was 260.34±50.06 µGy due to the approximate size of pets. Based on the results, the ESD value is associated with animal size, and larger animals have higher values. If a procedure doesn't require repetition, the dose can be optimized. For smaller animals, the main challenge in veterinary radiology is the dose increase caused by repetitions, which is most noticeable in the ventro-dorsal position due to the difficulty in immobilizing the animal.

Keywords: direct dose measuring, dosimetry, radiation protection, veterinary medicine

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Alimohamad Asghari, Ahmad Daneshi, Mohammad Farhadi, Arash Bayat, Mohammad Ajalloueyan, Marjan Mirsalehi, Mohsen Rajati, Seyed Basir Hashemi, Nader Saki, Ali Omidvari

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Evidence suggests that Cochlear Implantation (CI) is a beneficial approach for auditory and speech skills improvement in children with severe to profound hearing loss. However, it remains controversial if implantation in children <12 months is safe and effective compared to older children. The present study aimed to determine whether children's ages affect surgical complications and auditory and speech development. The current multicenter study enrolled 86 children who underwent CI surgery at <12 months of age (group A) and 362 children who underwent implantation between 12 and 24 months of age (group B). The Categories of Auditory Performance (CAP) and Speech Intelligibility Rating (SIR) scores were determined pre-impanation, and "one-year" and "two-year" post-implantation. Four complications (overall rate: 4.65%; three minor) occurred in group A and 12 complications (overall rate: 4.41%; nine minor) occurred in group B. We found no statistically significant difference in the complication rates between the groups (p>0.05). The mean SIR and CAP scores improved over time following CI activation in both groups. However, we did not find significant differences in CAP and SIR scores between the groups across different time points. Cochlear implantation is a safe and efficient procedure in children younger than 12 months, providing substantial auditory and speech benefits comparable to children undergoing implantation at 12 to 24 months of age. Furthermore, surgical complications in younger children are similar to those of children undergoing the CI at an older age.

Keywords: cochlear implant, Infant, complications, outcome

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Authors: Othoman Elsayah, Naren Gupta, Binsheng Zhang

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The construction sector constitutes one of the most important sectors in the economy of any country. Contractor selection is a critical decision that is undertaken by client organizations and is central to the success of any construction project. Contractor selection (CS) is a process which involves investigating, screening and determining whether candidate contractors have the technical and financial capability to be accepted to formally tender for construction work. The process should be conducted prior to the award of contract, characterized by many factors such as: contactor’s skills, experience on similar projects, track- record in the industry, and financial stability. However, this paper evaluates the current state of knowledge in relation to contractor selection process and demonstrates the findings from the analysis of the data collected from the Delphi questionnaire survey. The survey was conducted with a group of 12 experts working in the Libyan construction industry (LCI). The paper starts by briefly explaining the general outline of the questionnaire including the survey participation rate, the different fields the experts came from, and the business titles of the participants. Then, the paper describes the tests used to determine when the experts had reached consensus. The paper is based on research which aims to develop rank contractor selection criteria with specific application to make construction projects in the Libyan context. The findings of this study will be utilized to establish the scope of work that will be used as part of a PhD research.

Keywords: contractor selection, Libyan construction industry, decision experts, Delphi technique

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Authors: Valencia Fernandes, Dharmendra Kumar Khatri, Shashi Bala Singh

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Background and Aim: Epigenetics are the inaudible signatures of several pathological processes in the brain. This study understands the influence of DNA methylation, a major epigenetic modification, in the prefrontal cortex and hippocampus of the diabetic brain and its notable effect on the cellular chaperones and synaptic proteins. Method: Chronic high fat diet and STZ-induced diabetic mice were studied for cognitive dysfunction, and global DNA methylation, as well as DNA methyltransferase (DNMT) activity, were assessed. Further, the cellular chaperones and synaptic proteins were examined using DNMT inhibitor, 5-aza-2′-deoxycytidine (5-aza-dC)-via intracerebroventricular injection. Moreover, % methylation of these synaptic proteins were also studied so as to correlate its epigenetic involvement. Computationally, its interaction with the DNMT enzyme were also studied using bioinformatic tools. Histological studies for morphological alterations and neuronal degeneration were also studied. Neurogenesis, a characteristic marker for new learning and memory formation, was also assessed via the BrdU staining. Finally, the most important behavioral studies, including the Morris water maze, Y maze, passive avoidance, and Novel object recognition test, were performed to study its cognitive functions. Results: Altered global DNA methylation and increased levels of DNMTs within the nucleus were confirmed in the cortex and hippocampus of the diseased mice, suggesting hypermethylation at a genetic level. Treatment with AzadC, a global DNA demethylating agent, ameliorated the protein and gene expression of the cellular chaperones and synaptic fidelity. Furthermore, the methylation analysis profile showed hypermethylation of the hsf1 protein, a master regulator for chaperones and thus, confirmed the epigenetic involvement in the diseased brain. Morphological improvements and decreased neurodegeneration, along with enhanced neurogenesis in the treatment group, suggest that epigenetic modulations do participate in learning and memory. This is supported by the improved behavioral test battery seen in the treatment group. Conclusion: DNA methylation could possibly accord in dysregulating the memory-associated proteins at chronic stages in type 2 diabetes. This could suggest a substantial contribution to the underlying pathophysiology of several metabolic syndromes like insulin resistance, obesity and also participate in transitioning this damage centrally, such as cognitive dysfunction.

Keywords: epigenetics, cognition, chaperones, DNA methylation

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Authors: Karen Hong

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Smart tactile textiles are a series of textile-based products that incorporates smart embedded technology to be utilized as tactile therapeutic applications for 2 main groups of target users. The first group of users will be children with sensory processing disorder who are suffering from tactile sensory dysfunction. Children with tactile sensory issues may have difficulty tolerating the sensations generated from the touch of certain textures on the fabrics. A series of smart tactile textiles, collectively known as ‘Tactile Toys’ are developed as tactile therapy play objects, exposing children to different types of touch sensations within textiles, enabling them to enjoy tactile experiences together with interactive play which will help them to overcome fear of certain touch sensations. The second group of users will be the elderly or geriatric patients who are suffering from deteriorating sense of touch. One of the common consequences of aging is suffering from deteriorating sense of touch and a decline in motoric function. With the focus in stimulating the sense of touch for this particular group of end users, another series of smart tactile textiles, collectively known as ‘Tactile Aids’ are developed also as tactile therapy. This range of products can help to maintain touch sensitivity and at the same time allowing the elderly to enjoy interactive play to practice their hand-eye coordination and enhancing their motor skills. These smart tactile textile products are being designed and tested out by the end users and have proofed their efficacy as tactile therapy enabling the users to lead a better quality of life.

Keywords: smart textiles, embedded technology, tactile therapy, tactile aids, tactile toys

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Authors: M. De Iuliis, O. Kammouh, G. P. Cimellaro, S. Tesfamariam

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Community Resilience has gained a significant attention due to the recent unexpected natural and man-made disasters. Resilience is the process of maintaining livable conditions in the event of interruptions in normally available services. Estimating the resilience of systems, ranging from individuals to communities, is a formidable task due to the complexity involved in the process. The most challenging parameter involved in the resilience assessment is the 'downtime'. Downtime is the time needed for a system to recover its services following a disaster event. Estimating the exact downtime of a system requires a lot of inputs and resources that are not always obtainable. The uncertainties in the downtime estimation are usually handled using probabilistic methods, which necessitates acquiring large historical data. The estimation process also involves ignorance, imprecision, vagueness, and subjective judgment. In this paper, a fuzzy-based approach to estimate the downtime of building structures following earthquake events is proposed. Fuzzy logic can integrate descriptive (linguistic) knowledge and numerical data into the fuzzy system. This ability allows the use of walk down surveys, which collect data in a linguistic or a numerical form. The use of fuzzy logic permits a fast and economical estimation of parameters that involve uncertainties. The first step of the method is to determine the building’s vulnerability. A rapid visual screening is designed to acquire information about the analyzed building (e.g. year of construction, structural system, site seismicity, etc.). Then, a fuzzy logic is implemented using a hierarchical scheme to determine the building damageability, which is the main ingredient to estimate the downtime. Generally, the downtime can be divided into three main components: downtime due to the actual damage (DT1); downtime caused by rational and irrational delays (DT2); and downtime due to utilities disruption (DT3). In this work, DT1 is computed by relating the building damageability results obtained from the visual screening to some already-defined components repair times available in the literature. DT2 and DT3 are estimated using the REDITM Guidelines. The Downtime of the building is finally obtained by combining the three components. The proposed method also allows identifying the downtime corresponding to each of the three recovery states: re-occupancy; functional recovery; and full recovery. Future work is aimed at improving the current methodology to pass from the downtime to the resilience of buildings. This will provide a simple tool that can be used by the authorities for decision making.

Keywords: resilience, restoration, downtime, community resilience, fuzzy logic, recovery, damage, built environment

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Authors: Adel Edwar Waheeb Louka

Abstract:

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

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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