Search results for: nation image

Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez

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It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.

Keywords: education, evolutionary algorithms, evolution strategies, interactive learning applications

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Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors are capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore City. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By using an innovative image-processing algorithm, a new safety Measure of Effectiveness (MOE) was proposed to recognize the critical zones for bicyclists entering each zone. Considering the trajectory of conflicts, the results of the analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, post encroachment time threshold (PET), vehicle-bike conflicts, a measure of effectiveness (MOE), weather condition

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Authors: Selinda England, Ben Bodnaryk

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With an unprecedented increase in provincial immigration and government support for greater international and culturally diverse learners, a trade/applied learning-focused polytechnic with four campuses within one Canadian province saw the need for intercultural awareness and an intercultural teaching competence strategy for faculty training. An institution-wide pre-assessment needs survey was conducted in 2018, in which 87% of faculty professed to have some/no training when working with international and/or culturally diverse learners. After researching fellow Polytechnics in Canada and seeing very little in the way of faculty support for intercultural competence, an institutional project team comprised of members from all facets of the Polytechnic was created and included: Indigenous experts, Academic Chairs, Directors, Human Resource Managers, and international/settlement subject matter experts. The project team was organized to develop and implement a new academic model focused on enriching intercultural competence among faculty. Utilizing a competency based model, the project team incorporated inclusive terminology into competency indicators and devised a four-phase proposal for implementing intercultural teacher training: a series of workshops focused on the needs of international and culturally diverse learners, including teaching strategies based on current TESOL methodologies, literature and online resources for quick access when planning lessons, faculty assessment examples and models of interculturally proficient instructors, and future job descriptions - all which promote and encourage development of specific intercultural skills. Results from a post-assessment survey (to be conducted in Spring 2020) and caveats regarding improvements and next steps will be shared. The project team believes its intercultural and inclusive teaching competency-based model is one of the first, institution-wide faculty supported initiatives within the Canadian college and Polytechnic post-secondary educational environment; it aims to become a leader in both the province and nation regarding intercultural competency training for trades, industry, and business minded community colleges and applied learning institutions.

Keywords: cultural diversity and education, diversity training teacher training, teaching and learning, teacher training

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Authors: Atif Alenezi

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The low global oil price poses economic challenges for Saudi Arabia, as oil revenues still make up a great percentage of its Gross Domestic Product (GDP). At the end of 2014, the Consultative Assembly considered a report from the Committee on Economic Affairs and Energy which highlights that the economy had not been successfully diversified. There thus exist ample reasons for modernising the Foreign Direct Investment (FDI) regime, primarily to achieve and maintain prosperity and facilitate peace in the region. Therefore, this paper aims at identifying specific problems with the existing FDI regime in Saudi Arabia and subsequently some solutions to those problems. Saudi Arabia adopted its first specific legislation in 1956, which imposed significant restrictions on foreign ownership. Since then, Saudi Arabia has modernised its FDI framework with the passing of the Foreign Capital Investment Act 1979 and the Foreign Investment Law2000 and the accompanying Executive Rules 2000 and the recently adopted Implementing Regulations 2014.Nonetheless, the legislative provisions contain various gaps and the failure to address these gaps creates risks and uncertainty for investors. For instance, the important topic of mergers and acquisitions has not been addressed in the Foreign Investment Law 2000. The circumstances in which expropriation can be considered to be in the public interest have not been defined. Moreover, Saudi Arabia has not entered into many bilateral investment treaties (BITs). This has an effect on the investment climate, as foreign investors are not afforded typical rights. An analysis of the BITs which have been entered into reveals that the national treatment standard and stabilisation, umbrella or renegotiation provisions have not been included. This is problematic since the 2000 Act does not spell out the applicable standard in accordance with which foreign investors should be treated. Moreover, the most-favoured-nation (MFN) or fair and equitable treatment (FET) standards have not been put on a statutory footing. Whilst the Arbitration Act 2012 permits that investment disputes can be internationalised, restrictions have been retained. The effectiveness of international arbitration is further undermined because Saudi Arabia does not enforce non-domestic arbitral awards which contravene public policy. Furthermore, the reservation to the Convention on the Settlement of Investment Disputes allows Saudi Arabia to exclude petroleum and sovereign disputes. Interviews with foreign investors, who operate in Saudi Arabia highlight additional issues. Saudi Arabia ought not to procrastinate far-reaching structural reforms.

Keywords: FDI, Saudi, BITs, law

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Authors: Yumee Kim, Seung-Guk Hyeon, Kukjin Chun

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An adjustable aperture using a liquid crystal is proposed for real-time range detection and obtaining images simultaneously. The adjustable aperture operates as two types of aperture stops which can create two different Depth of Field images. By analyzing these two images, the distance can be extracted from camera to object. Initially, the aperture stop has large size with zero voltage. When the input voltage is applied, the aperture stop transfer to smaller size by orientational transition of liquid crystal molecules in the device. The diameter of aperture stop is 1.94mm and 1.06mm. The proposed device has low driving voltage of 7.0V and fast response time of 6.22m. Compact size aperture of 6×6×1.1 mm3 is assembled in conventional camera which contain 1/3” HD image sensor and focal length of 3.3mm that can be used in autonomous. The measured range was up to 5m. The adjustable aperture has high stability due to no mechanically moving parts. This range sensor can be applied to the various field of 3D depth map application which is the Advanced Driving Assistance System (ADAS), drones and manufacturing machine.

Keywords: adjustable aperture, dual aperture, liquid crystal, ranging and imaging, ADAS, range sensor

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Authors: Irma Wani Othman

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The internationalization of higher education in Malaysia mainly focuses to place the implementation of the strategic, comprehensive and integrated range of stakeholders in order to highlight the visibility of Malaysia as a hub of academic excellence. While the concept of 'global citizenship' is used as a two-pronged strategy of aggressive marketing by universities which includes; (i) the involvement of the academic expatriates in stimulating international activities of higher education and (ii) an increase in international student enrollment capacity for the enculturation of science and the development of first class mentality. In this aspect, aspirations for a transnational social movement through global citizenship status to establish the identity of the university community without borders (borderless universities) - regardless of skin colour, thus rationalize and liberalize the universal principles of life and cultural traditions of a nation. The education system earlier referred by the spirit of nationalism is now progressing due to globalization, hence forming a system of higher education that is relevant and generated by the need of all time. However, debates arose when the involvement of global citizenship is said to threaten the ultimate university autonomy in determining the direction of academic affairs and governance of their human resources. Stemming from this debate, this study aims to explore the experience of 'global citizenship' that the academic expatriates and international students in shaping the university's strategic needs and interests which are in line with the transition of contemporary higher education. The objective of this study is to examine the acculturation experience of the global citizen in the form of transnational higher education system and suggest policy and policing IHE which refers directly to the experience of the global citizen. This study offers a detailed understanding of how the university communities assess their expatriation experience, thus becoming useful information for learning and transforming education. The findings also open an advanced perspective on the international mobility of human resources and the implications on the implementation of the policy of internationalization of higher education. The contribution of this study is expected to give new input, thus shift the focus of contextual literature for the internationalization of the education system. Instead of focusing on the purpose of generating income of a university, to a greater understanding of subjective experience in utilizing international human resources hence contributing to the prominent transnational character of higher education.

Keywords: internationalization, global citizens, Malaysia higher education, academic expatriate, international students

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Authors: Isabel Jones

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As adolescence is a formative time, experiences during adolescence often affect the rest of one’s life. Therefore, dating, specifically violence in dating, can have lasting effects on the rest of one’s life. In order to find sources, searches were conducted on PsycINFO, specifically EBSCO, and narrowed down under the criteria that the source contained information about adolescent dating violence rather than adult, and focused on causes, effects, or prevention methods. This literature review examines research regarding the effects and causes of TDV, and then what methods are effective in the prevention of TDV development. This will allow for a clear image of how these prevention methods are effective and why they are important. Effects of TDV extend beyond the physical, including psychological and sexual long-lasting effects. These are caused by a number of concepts, including learned behavior, inhibitory issues/substance abuse, and cultural factors. When both of these are taken into account, preventative measures such as school-based interventions, parental/adult monitoring, and the presence of positive family examples are more clear as to their effectiveness. This literature review may provide further awareness to this public health crisis and give the public a view of how adolescents are affected by TDV on their path from child to adult.

Keywords: adolescence, dating violence, risk factors, predictors, relationship

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Authors: Linda Jane Wells

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In 1914 Topsy Smith, a woman of the First Nations Arabana tribe arrived in Alice Springs with her seven children and a herd of goats. They had come in from the goldfields at Arltunga where they had been living, and Topsy’s husband, Welsh-born Bill Smith, had recently died. Sergeant Stott, the local policeman and sub-protector of Aborigines for the region, erected a tin shed for Topsy and the children to live in, which became known as the Bungalow for half-castes. Over the years that followed many more children of mixed descent were removed from their families and brought to live at the Bungalow until, a decade later, sixty children were growing up there, cared for predominantly by Topsy Smith; Ida Standley who was the first, white schoolteacher for the town; and Sergeant Stott. The story of the Bungalow is pivotal to the foundations of social relations in the town of Alice Springs and beyond. At the same time, it is little known, recognised or understood locally, let alone more broadly. This is typical of the dominant historic narratives that have emerged out of the Australian colonial project and led to ‘the Great Australian Silence.’ The term was coined by Australian anthropologist WEH Stanner in his 1968 Boyer Lectures, in reference to the omission of the Aboriginal experience from the dominant narratives of the nation’s history. In his lecture, he attributed this silence to something that may have begun as a simple forgetting of other possible views which turned, under habit and over time, into something like a cult of forgetfulness practised on a national scale. This doctoral project, underpinned by a methodology of practice-led research, engages a bricolage of methods including archival research, ethnography, and oral histories to research the bungalow and the context in which it operated. Techniques of fictocriticism, speculative biography, autoethnography, and archival poetics are then engaged to write the research outcomes into a post-colonial, multi-genre work of creative non-fiction that speaks into the silences in the archives. The overall intent of this doctoral work is to explore and demonstrate how techniques of creative non-fiction can be used to rewrite narratives of Australian colonial history that resonate beyond the academy, thus contributing to the bank of post-colonial stories and working towards a more just, honest and inclusive national ‘memory’ and identity.

Keywords: Australian history, collective memory, creative non-fiction, postcolonialism

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

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

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

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Authors: Ramkumar Gunasekaran, John Cater, Vinod Suresh, Haribalan Kumar

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The main objective of this project is to develop a statistical shape model using principal component analysis that could be used for analyzing the shape of the human airway. The ultimate goal of this project is to identify geometric risk factors for diagnosis and management of Obstructive Sleep Apnoea (OSA). Anonymous CBCT scans of 25 individuals were obtained from the Otago Radiology Group. The airways were segmented between the hard-palate and the aryepiglottic fold using snake active contour segmentation. The point data cloud of the segmented images was then fitted with a bi-cubic mesh, and pseudo landmarks were placed to perform PCA on the segmented airway to analyze the shape of the airway and to find the relationship between the shape and OSA risk factors. From the PCA results, the first four modes of variation were found to be significant. Mode 1 was interpreted to be the overall length of the airway, Mode 2 was related to the anterior-posterior width of the retroglossal region, Mode 3 was related to the lateral dimension of the oropharyngeal region and Mode 4 was related to the anterior-posterior width of the oropharyngeal region. All these regions are subjected to the risk factors of OSA.

Keywords: medical imaging, image processing, FEM/BEM, statistical modelling

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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

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

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

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Authors: A. W. Chalmers, O. M. van den Broek

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This article examines the relationship between the global financial crisis and corporate social responsibility activities of financial services firms. It challenges the general consensus in existing studies that firms, when faced with economic hardship, tend to jettison CSR commitments. Instead, and building on recent insights into the institutional determinants of CSR, it is argued that firms are constrained in their ability to abandon CSR by the extent to which they are subject to intense public scrutiny by regulators and the news media. This argument is tested in the context of the European sovereign debt crisis drawing on a unique dataset of 170 firms in 15 different countries over a six-year period. Controlling for a battery of alternative explanations and comparing financial service providers to firms operating in other economic sectors, results indicate considerable evidence supporting the main argument. Rather than abandoning CSR during times of economic hardship, financial industry firms ramp up their CSR commitments in order to manage their public image and foster public trust in light of intense public scrutiny.

Keywords: corporate social responsibility (CSR), public scrutiny, global financial crisis, financial services firms

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Authors: Luo Jin China, Jin Fang

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Countryside has been generally recognized and regarded as a characteristic symbol which presents in human memory for a long time. As a result of the change of times, because of it’s failure to meet the growing needs of the growing life and mental decline, the vast rural area began to decline. But their history feature image which accumulated by the ancient tradition provides people with the origins of existence on the spiritual level, such as "identity" and "belonging", makes people closer to the others in the spiritual and psychological aspects of a common experience about the past, thus the sense of a lack of culture caused by the losing of memory symbols is weakened. So, in the modernization process, how to repair its vitality and transform and planning it in a sustainable way has become a hot topics in architectural and urban planning. This paper aims to break the constraints of disciplines, from the perspective of interdiscipline, using the research methods of systems science to analyze and discuss the theories and methods of rural form factors, which based on the viewpoint of memory in psychology. So, we can find a right way to transform the Rural to give full play to the role of the countryside in the actual use and the shape of history spirits.

Keywords: rural, sustainable renovation, restoration, psychology, memory

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Authors: Khaled Missaoui, Amina Beldjazia, Rachid Gharzouli, Yamna Djellouli

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Optical remote sensing makes use of visible, near infrared and short-wave infrared sensors to form images of the earth's surface by detecting the solar radiation reflected from targets on the ground. Different materials reflect and absorb differently at different wavelengths. Thus, the targets can be differentiated by their spectral reflectance signatures in the remotely sensed images. In this work, we are interested to study the distribution of vegetation in the massif forest of Boutaleb (North East of Algeria) which suffered between 1998 and 1999 very large fires. In this case, we use remote sensing with Landsat images from two dates (1984 and 2000) to see the results of these fires. Vegetation has a unique spectral signature which enables it to be distinguished readily from other types of land cover in an optical/near-infrared image. Normalized Difference Vegetation Index (NDVI) is calculated with ENVI 4.7 from Band 3 and 4. The results showed a very important floristic diversity in this forest. The comparison of NDVI from the two dates confirms that there is a decrease of the density of vegetation in this area due to repeated fires.

Keywords: remote sensing, boutaleb, diversity, forest

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Authors: T. J. Hasanova, C. N. Imamalieva

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The problem about the movement of the rigid cylinder keeping the vertical position under the influence of running superficial waves in a liquid is considered. The indignation of a falling wave caused by the presence of the cylinder which moves is thus considered. Special decomposition on a falling harmonious wave is used. The problem dares an operational method. For a finding of the original decision, Considering that the image denominator represents a tabular function, Voltaire's integrated equation of the first sort which dares a numerical method is used. Cylinder movement in the continuous environment under the influence of waves is considered in work. Problems are solved by an operational method, thus originals of required functions are looked for by the numerical definition of poles of combinations of transcendental functions and calculation of not own integrals. Using specificity of a task below, Decisions are under construction the numerical solution of the integrated equation of Volter of the first sort that does not create computing problems of the complex roots of transcendental functions connected with search.

Keywords: rigid cylinder, linear interpolation, fluctuations, Voltaire's integrated equation, harmonious wave

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Authors: Zainab S. Abdullahi, Zakariyya H. Abdullahi, Sahnun Dahiru

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Student attendance in schools is a very important aspect in school management record. In recent years, security systems have become one of the most demanding systems in school. Every institute have its own method of taking attendance, many schools in Nigeria use the old fashion way of taking attendance. That is writing the students name and registration number in a paper and submitting it to the lecturer at the end of the lecture which is time-consuming and insecure, because some students can write for their friends without the lecturer’s knowledge. In this paper, we propose a system that takes attendance using face recognition. There are many automatic methods available for this purpose i.e. biometric attendance, but they all waste time, because the students have to follow a queue to put their thumbs on a scanner which is time-consuming. This attendance is recorded by using a camera attached in front of the class room and capturing the student images, detect the faces in the image and compare the detected faces with database and mark the attendance. The principle component analysis was used to recognize the faces detected with a high accuracy rate. The paper reviews the related work in the field of attendance system, then describe the system architecture, software algorithm and result.

Keywords: attendance system, face detection, face recognition, PCA

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Authors: Ruthanne Tobin

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As our data-driven, cloud-based, knowledge-centric lives become ever more global, mobile, and digital, educational systems everywhere are struggling to keep pace. Schools need to prepare students to become critical-thinking, tech-savvy, life-long learners who are engaged and adaptable enough to find their unique calling in a post-industrial world of work. Recognizing that no nation can afford poor achievement or high dropout rates without jeopardizing its social and economic future, the thirty-two nations of the OECD are launching initiatives to redesign schools, generally under the banner of Personalized Learning or 21st Century Learning. Their intention is to transform education by situating students as co-enquirers and co-contributors with their teachers of what, when, and how learning happens for each individual. In this focused review of the 2007-2017 literature on personalized learning, the author sought answers to two main questions: “What are the theoretical frameworks that guide personalized learning?” and “What is the conceptual understanding of the model?” Ultimately, the review reveals that, although the research area is overly theorized and under-substantiated, it does provide a significant body of knowledge about this potentially transformative educational restructuring. For example, it addresses the following questions: a) What components comprise a PL model? b) How are teachers facilitating agency (voice & choice) in their students? c) What kinds of systems, processes and procedures are being used to guide the innovation? d) How is learning organized, monitored and assessed? e) What role do inquiry based models play? f) How do teachers integrate the three types of knowledge: Content, pedagogical and technological? g) Which kinds of forces enable, and which impede, personalizing learning? h) What is the nature of the collaboration among teachers? i) How do teachers co-regulate differentiated tasks? One finding of the review shows that while technology can dramatically expand access to information, expectations of its impact on teaching and learning are often disappointing unless the technologies are paired with excellent pedagogies in order to address students’ needs, interests and aspirations. This literature review fills a significant gap in this emerging field of research, as it serves to increase conceptual clarity that has hampered both the theorizing and the classroom implementation of a personalized learning model.

Keywords: curriculum change, educational innovation, personalized learning, school reform

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Authors: Ali Ben Abbes, Imed Riadh Farah

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Due to the considerable advances in Multi-Temporal Satellite Images (MTSI), remote sensing application became more accurate. Recently, many advances in modeling MTSI are developed using various models. The purpose of this article is to present an overview of studies using Hidden Markov Model (HMM). First of all, we provide a background of using HMM and their applications in this context. A comparison of the different works is discussed, and possible areas and challenges are highlighted. Secondly, we discussed the difference on vegetation monitoring as well as urban growth. Nevertheless, most research efforts have been used only stationary data. From another point of view, in this paper, we describe a new non-stationarity HMM, that is defined with a set of parts of the time series e.g. seasonal, trend and random. In addition, a new approach giving more accurate results and improve the applicability of the HMM in modeling a non-stationary data series. In order to assess the performance of the HMM, different experiments are carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern region of Tunisia and Landsat time series of tres Cantos-Madrid in Spain.

Keywords: multi-temporal satellite image, HMM , nonstationarity, vegetation, urban

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Ahmed S. Afifi, Ahmed Magdy

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Satellite monitoring is an essential tool to study, understand, and map large-scale environmental changes that affect humans, climate, and biodiversity. The Sentinel-1 Synthetic Aperture Radar (SAR) instrument provides a high collection of data in all-weather, short revisit time, and high spatial resolution that can be used effectively in flood management. Floods occur when an overflow of water submerges dry land that requires to be distinguished from flooded areas. In this study, we use global flood mapper (GFM), a new google earth engine application that allows users to quickly map floods using Sentinel-1 SAR. The GFM enables the users to adjust manually the flood map parameters, e.g., the threshold for Z-value for VV and VH bands and the elevation and slope mask threshold. The composite R:G:B image results by coupling the bands of Sentinel-1 (VH:VV:VH) reduces false classification to a large extent compared to using one separate band (e.g., VH polarization band). The flood mapping algorithm in the GFM and the Otsu thresholding are compared with Sentinel-2 optical data. And the results show that the GFM algorithm can overcome the misclassification of a flooded area in An Giang, Vietnam.

Keywords: SAR backscattering, Sentinel-1, flood mapping, disaster

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Authors: Linghua Kong, Xinxia Chen, Mika Gissler, Catharina Lavebratt

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Background: Prenatal exposure to preeclampsia has been associated with an increased risk of offspring attention-deficit/hyperactivity disorders (ADHD), autism spectrum disorder (ASD), and intellectual disability. However, little is known about the association between prenatal exposure to severe preeclampsia and neurodevelopmental and psychiatric disorders in offspring. Objective: This study aimed to assess the risk of maternal preeclampsia combined with perinatal problems, specifically low birth weight and prematurity, on offspring neuropsychiatric disorders. Methods: All singleton live births in Finland between 1996 and 2014 (n=1 012 723) were followed up in nation-wide registries until 2018. Main exposures included pre-eclampsia, small for gestational age, and delivery before 34 gestational weeks. Offspring neurodevelopmental and psychiatric disorders (ICD-10 codes) were examined as outcomes variables. Offspring birth year, sex, maternal age at delivery, parity, marital status at birth, mother's country of birth, maternal smoking, maternal gestational diabetes, maternal use of psychotropic medication during pregnancy, and maternal systemic inflammatory diseases were used as covariates. Risks for neurodevelopmental and psychiatric disorders were estimated using Cox proportional hazards modeling. Results: Of the 1 012 723 offspring, 25 901 (2.6%) were exposed to preeclampsia, and 93 281 (9.2%) were diagnosed with a neuropsychiatric disorder. Compared to births unexposed to preeclampsia, small for gestational age or delivery before 34 gestational weeks, those exposed to preeclampsia only had a 21% increase in the likelihood of any neuropsychiatric disorders after adjusting for potential confounding (adjusted HR=1.21, 95% CI: 1.15-1.26), while exposure to preeclampsia combined with small for gestational age or delivery before 34 gestational weeks had a more than twofold increased risk of having a child with neuropsychiatric disorders (adjusted HR=2.16, 95% CI: 2.02-2.32). The adjusted HR for neuropsychiatric disorders in offspring with small for gestational age or delivery before 34 gestational weeks only was 1.79 (95% CI: 1.73-1.83). In addition, the risk estimate in offspring exposed to both preeclampsia and perinatal problems was greater than those only exposed to preeclampsia for having personality disorders (adjusted HR=1.66; 95% CI: 1.07-2.57), intellectual disabilities (adjusted HR=3.47; 95% CI: 2.86-4.22), specific developmental disorders (adjusted HR=2.91; 95% CI: 2.69-3.15), ASD (adjusted HR=1.75; 95% CI: 1.42-2.17), ADHD and conduct disorders (adjusted HR=2.00; 95%CI: 1.76-2.27), and other behavioral and emotional disorders (adjusted HR=2.09; 95% CI: 1.84-2.37). Conclusion: In utero exposure to severe preeclampsia increased the risk of several neurodevelopmental and psychiatric disorders in offspring. Our findings are relevant to women with hypertensive disorders with regard to pregnancy consultation and management and may yield effective clues for the prevention of neurodevelopmental and psychiatric disorders in childhood.

Keywords: low birth weight, neurodevelopmental disorders, preeclampsia, prematurity, psychiatric disorders

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Josephine Aikpitanyi, Friday Okonofua, Lorrettantoimo, Sandy Tubeuf

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Every day, 800 women die from conditions related to pregnancy and childbirth, resulting in an estimated 300,000 maternal deaths worldwide per year. Over 99 percent of all maternal deaths occur in developing countries, with more than half of them occurring in sub-Saharan Africa. Nigeria being the most populous nation in sub-Saharan Africa bears a significant burden of worsening maternal and child health outcomes with a maternal mortality rate of 917 per 100,000 live births and child mortality rate of 117 per 1,000 live births. While several studies have documented that financial barriers disproportionately discourage poor women from seeking needed maternal and child healthcare, other studies have indicated otherwise. Evidence shows that there are instances where health facilities with skilled healthcare providers exist, and yet maternal, and child health outcomes remain abysmally low, indicating the presence of non-cognitive and behavioural factors that may affect the utilization of healthcare services. This study investigated the influence of locus of control and self-esteem on utilization of maternal and child healthcare services in Nigeria. Specifically, it explored the differences in utilization of antenatal care, skilled birth care, postnatal care, and child vaccination by women having an internal and external locus of control and women having high and low self-esteem. We collected information on non-cognitive traits of 1411 randomly selected women, along with information on utilization of the various indicators of maternal and child healthcare. Estimating logistic regression models for various components of healthcare services utilization, we found that women’s internal locus of control was a significant predictor of utilization of antenatal care, skilled birth care, and completion of child vaccination. We also found that having high self-esteem was a significant predictor of utilization of antenatal care, postnatal care, and completion of child vaccination after adjusting for other control variables. By improving our understanding of non-cognitive traits as possible barriers to maternal and child healthcare utilization, our findings offer important insights for enhancing participant engagement in intervention programs that are initiated to improve maternal and child health outcomes in low-and-middle-income countries.

Keywords: behavioural economics, health-seeking behaviour, locus of control and self-esteem, maternal and child healthcare, non-cognitive traits, and healthcare utilization

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: Afees Adebayo Salam

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This paper attempts an in-depth analysis of the economic impact of Ogbomoso migrant community in the Jos metropolis. It discusses the factors that motivated a sizeable number of Ogbomoso people (from southwestern Nigeria) to leave their hometown for a new place/space in Jos (northern Nigeria). It examines the historical antecedent of Ogbomoso migrants in northern Nigeria with emphasis on Jos metropolis. The movement of Ogbomoso migrants to Jos was dictated by the economic and social challenges of colonial and post-colonial periods. The political crisis of the 1960s was a contributory factor to the process of Ogbomoso migration to other parts of Nigeria. In the aftermath, many people migrated from Ogbomoso to different parts of the country and beyond to seek for better economic opportunities. The establishment of Ogbomoso migrant community in Jos was dated back to the colonial era when taxation was introduced by the British. Many people could not pay these taxes from their peasant farming activities, while some embarked on migration to places such as Jos, Kaduna, Kano, Keffi and Bauchi due to the harsh economic situation at home. Their settlement in Jos brought about success in several spheres of human endeavours. Ogbomoso migrants dominated both paid jobs and private business sector such as textile merchants, food stuff sellers, herbalists, printers, transporters, and religious missionaries, as well as clerical officers in the government establishments. Their remittances were invested in different sectors of Ogbomoso economy. The migrants had in one way or the other contributed to the socio-economic development of their host community in Jos as entrepreneurs. Branches of such industries were located in their hometown of Ogbomoso as a clear demonstration of community development. The remittance pattern of the migrants has transformed Ogbomoso to enviable position. Moreover, the economic success of Ogbomoso migrants over the period under review indicates the process of nation building due to peaceful nature of inter-ethnic engagements between Ogbomoso migrants and their host community in Jos. Therefore, the paper makes use of oral, archival and secondary sources to analyse the processes of migration and its economic impact. Oral interviews were conducted in Ogbomoso town with veteran migrants and their family members. Interviews were also conducted in Jos with the indigenous host community as well as other urban residents. Archival materials were obtained from Arewa House Archives and the National Archives, Kaduna and the National Archives, Ibadan.

Keywords: Ogbomoso migrants, Jos metropolis, community development, economic impact

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Authors: Titik Wahyuningsih

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The folktale to discuss is Ande-Ande Lumut, a story that is believed to be a history of two kingdoms in East Java, Indonesia. The title refers to the main male character in the story. This research is a library research which aims to know the patriarchal values in Indonesia. The data for the research is the song in the story that is actually the conversation between Ande-Ande Lumut and a mom who adopts him. It is told in the lines that many beautiful girls come to propose Ande-Ande Lumut but he does not want to accept them and keeps on staying in his upstairs room. Finally, he says yes for Klething Kuning to whom his mom describes as a girl with ugly face. Ande-Ande Lumut's decision is taken as Klething Kuning is the only girl who doesn't let Yuyu Kangkang help her. Yuyu Kangkang is described as a very big crab that helps the girls to cross the river but ask them to kiss him. Through the lense of feminist approach, Ande-Ande Lumut shows the men’s preference and dominance to make final decision over women. Even though the girls are actively propose their future husband, but they do it without giving any requirements. Meanwhile, the future husband chooses a girl with a criterion that no male has ever touched her, although the male is a crab.

Keywords: future spouse, Indonesian folktale, acceptance, patriarchal

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Authors: Haowen Xu, Xiaobing Liu, Jin Dong, Jianming Lian

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Emerging smart buildings often employ cyberinfrastructure, cyber-physical systems, and Internet of Things (IoT) technologies to increase the automation and responsiveness of building operations for better energy efficiency and lower carbon emission. These operations include the control of Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems, which are often considered a major source of energy consumption in both commercial and residential buildings. Developing energy-saving control models for optimizing HVAC operations usually requires the collection of high-quality instrumental data from iterations of in-situ building experiments, which can be time-consuming and labor-intensive. This abstract describes a digital twin approach to automate building energy experiments for optimizing HVAC operations through the design and development of an adaptive web-based platform. The platform is created to enable (a) automated data acquisition from a variety of IoT-connected HVAC instruments, (b) real-time situational awareness through domain-based visualizations, (c) adaption of HVAC optimization algorithms based on experimental data, (d) sharing of experimental data and model predictive controls through web services, and (e) cyber-physical control of individual instruments in the HVAC system using outputs from different optimization algorithms. Through the digital twin approach, we aim to replicate a real-world building and its HVAC systems in an online computing environment to automate the development of building-specific model predictive controls and collaborative experiments in buildings located in different climate zones in the United States. We present two case studies to demonstrate our platform’s capability for real-time situational awareness and cyber-physical control of the HVAC in the flexible research platforms within the Oak Ridge National Laboratory (ORNL) main campus. Our platform is developed using adaptive and flexible architecture design, rendering the platform generalizable and extendable to support HVAC optimization experiments in different types of buildings across the nation.

Keywords: energy-saving buildings, digital twins, HVAC, cyber-physical system, BIM

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