Search results for: project based learning

Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: Machine learning, medical diagnosis, meningitis detection, gradient boosting.

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Authors: J. Fenianos, C. Khater, D. Brouillet

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Ecological projects are often faced with reluctance from local communities hosting the project, especially when this project involves variation from preset ideas or classical practices. This paper aims at appreciating the contribution of environmental psychology through cognitive flexibility exercises to improve the acceptability of local communities in adopting more ecological rehabilitation scenarios. The study is based on a quarry site located in Bekaa- Lebanon. Four groups were considered with different levels of involvement, as follows: Group 1 is Training (T) – 50 hours of on-site training over 8 months, Group 2 is Awareness (A) – 2 hours of awareness raising session, Group 3 is Flexibility (F) – 2 hours of flexibility exercises and Group 4 is the Control (C). The results show that individuals in Group 3 (F) who followed flexibility sessions accept comparably the ecological rehabilitation option over the more classical one. This is also the case for the people in Group 1 (T) who followed a more time-demanding “on-site training”. Another experience was conducted on a second quarry site combining flexibility with awareness-raising. This research confirms that it is possible to reduce resistance to change thanks to a limited in-time intervention using cognitive flexibility. This methodological approach could be transferable to other environmental problems involving local communities and changes in preset perceptions.

Keywords: Acceptability, ecological restoration, environmental psychology, Lebanon, local communities, resistance to change.

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Authors: Bashaiar Alsanaa

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Never has a revolution affected all aspects of humanity as the communication revolution during the past two decades. This revolution, with all its advances and utilities, swept the world thus becoming an integral part of our lives, hence giving way to emerging applications at the social, economic, political, and educational levels. More specifically, such applications have changed the delivery system through which learning is acquired by students. Interaction with educators, accessibility to content, and creative delivery options are but a few facets of the new learning experience now being offered through the use of technology in the educational field. With different success rates, third world countries have tried to pace themselves with use of educational technology in advanced parts of the world. One such country is the small rich-oil state of Kuwait which has tried to adopt the e-educational model, however, an evaluation of such trial is yet to be done. This study aimed to fill the void of research conducted around that topic. The study explored students' acceptance of incorporating communication technologies in higher education in Kuwait. Students' responses to survey questions presented an overview of the e-learning experience in this country, and drew a framework through which implications and suggestions for future research were discussed to better serve the advancement of e-education in developing countries.

Keywords: Communication technologies, E-learning, Kuwait, Social media

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Regius Asiimwe, Amir Anvar

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This paper describes the process used in the automation of the Maritime UAV commands using the Kinect sensor. The AR Drone is a Quadrocopter manufactured by Parrot [1] to be controlled using the Apple operating systems such as iPhones and Ipads. However, this project uses the Microsoft Kinect SDK and Microsoft Visual Studio C# (C sharp) software, which are compatible with Windows Operating System for the automation of the navigation and control of the AR drone. The navigation and control software for the Quadrocopter runs on a windows 7 computer. The project is divided into two sections; the Quadrocopter control system and the Kinect sensor control system. The Kinect sensor is connected to the computer using a USB cable from which commands can be sent to and from the Kinect sensors. The AR drone has Wi-Fi capabilities from which it can be connected to the computer to enable transfer of commands to and from the Quadrocopter. The project was implemented in C#, a programming language that is commonly used in the automation systems. The language was chosen because there are more libraries already established in C# for both the AR drone and the Kinect sensor. The study will contribute toward research in automation of systems using the Quadrocopter and the Kinect sensor for navigation involving a human operator in the loop. The prototype created has numerous applications among which include the inspection of vessels such as ship, airplanes and areas that are not accessible by human operators.

Keywords: UAV, AR drone, Kinect Sensors, Automation, Real time, C sharp, Microsoft Kinect SDK.

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Authors: Indra Budi, Rizal Fathoni Aji, Agus Widodo

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Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.

Keywords: Combination, emerging topics, ensemble, forecasting, machine learning, prediction, research topics, similarity measure, time series.

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: Electrocardiogram, dictionary learning, sparse coding, classification.

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Authors: M. A. Zahari, S. S. Dol

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The global demand for continuous and eco-friendly renewable energy as alternative to fossils fuels is large and ever growing in nowadays. This paper will focus on capability of Vortex Induced Vibration (VIV) phenomenon in generating alternative energy for offshore platform application. In order to maximize the potential of energy generation, the effects of lock in phenomenon and different geometries of cylinder were studied in this project. VIV is the motion induced on bluff body which creates alternating lift forces perpendicular to fluid flow. Normally, VIV is unwanted in order to prevent mechanical failure of the vibrating structures. But in this project, instead of eliminating these vibrations, VIV will be exploited to transform these vibrations into a valuable resource of energy.

Keywords: Vortex Induced Vibration, Vortex Shedding, Renewable Energy

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Authors: Cory A. Bennett

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Teaching and learning through the use of discourse support students’ conceptual understanding by attending to key concepts and relationships. One discourse structure used in primary classrooms is number talks wherein students mentally calculate, discuss, and reason about the appropriateness and efficiency of their strategies. In the secondary mathematics classroom, the mathematics understudy does not often lend itself to mental calculations yet learning to reason, and articulate reasoning, is central to learning mathematics. This qualitative case study discusses how one secondary school in the Middle East adapted the number talk protocol for secondary mathematics classrooms. Several challenges in implementing ‘reasoning talks’ became apparent including shifting current discourse protocols and practices to a more student-centric model, accurately recording and probing student thinking, and specifically attending to reasoning rather than computations.

Keywords: Discourse, reasoning, secondary mathematics, teacher development.

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Authors: A. Otjen, S. Keller

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A Montana university has used applied consumer research in experiential learning with non-profit clients for over a decade. Through trial and error, a successful protocol has been established from problem statement through formative research to integrated marketing campaign execution. In this paper, we describe the protocol and its applications. Analysis was completed to determine the effectiveness of the campaigns and the results of how pre- and post-consumer research mark societal change because of media.

Keywords: Marketing, experiential learning, consumer behavior, community partner.

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Authors: Cristina M. R. Caridade, Ana Rita F. Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with health care facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool, developed in MATLAB using Graphical User Interface, for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing to view original images and fusion images, compare processed and original images, adjust parameters and save images. The tool proposed in an innovative teaching and learning environment, consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques, necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides a real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: Image fusion, image processing, teaching-learning simulation tool, biomedical engineering education.

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Authors: Girija Chetty, Monica Singh

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In this paper we propose a novel approach for ascertaining human identity based on fusion of profile face and gait biometric cues The identification approach based on feature learning in PCA-LDA subspace, and classification using multivariate Bayesian classifiers allows significant improvement in recognition accuracy for low resolution surveillance video scenarios. The experimental evaluation of the proposed identification scheme on a publicly available database [2] showed that the fusion of face and gait cues in joint PCA-LDA space turns out to be a powerful method for capturing the inherent multimodality in walking gait patterns, and at the same time discriminating the person identity..

Keywords: Biometrics, gait recognition, PCA, LDA, Eigenface, Fisherface, Multivariate Gaussian Classifier

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Authors: Sorana M. Manoiu, Razvan V. Mustata, Jiří Strouhal, Carmen G. Bonaci, Dumitru Matis, Jiřina Bokšová

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In the current context of globalization, accountability has become a key subject of real interest for both, national and international business areas, due to the need for comparability and transparency of the economic situation, so we can speak about the harmonization and convergence of international accounting. The paper presents a qualitative research through content analysis of several reports concerning the roadmap for convergence. First, we develop a conceptual framework for the evolution of standards’ convergence and further we discuss the degree of standards harmonization and convergence between US GAAP and IAS/IFRS as to October 2012. We find that most topics did not follow the expected progress. Furthermore there are still some differences in the long-term project that are in process to be completed and other that were reassessed as a lower priority project.

Keywords: Convergence, harmonization, FASB, IASB, IFRS, US GAAP.

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: Neural networks, pattern learning, security, wireless sensor networks.

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Authors: Fiasal Assiri, Joanna Wincenciak, David Morrison-Love

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With the rapid spread of the COVID-19 pandemic, the Saudi government suspended students from going to school to combat the outbreak. As e-learning was not applied at all in schools, online teaching and learning have been revived in Saudi Arabia by providing a new platform called ‘Madrasati’. The Decomposed Theory of Planned Behaviour (DTPB) is used to examine individuals’ intention behaviour in many fields. Nevertheless, the factors that affect teachers’ continuance intention of the Madrasati platform have not yet been investigated. The purpose of this paper is to present a conceptual model in light with DTPB. To enhance the predictability of the model, the study incorporates other variables including learning content quality and interactivity as sub-factors under the perceived usefulness, students and government influences under the subjective norms, and technical support and prior e-learning experience under the perceived behavioural control. The model will be further validated using a mixed methods approach. Such findings would help administrators and stakeholders to understand teachers’ needs and develop new methods that might encourage teachers to continue using Madrasati effectively in their teaching.

Keywords: Madrasati, Decomposed Theory of Planned Behaviour, continuance intention, attitude, subjective norms, perceived behavioural control.

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Authors: A. Kourou, A. Ioakeimidou, V. Avramea

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Planning for natural disasters and emergencies is something every school or educational institution must consider, regardless of its size or location. Preparedness is the key to save lives if a disaster strikes. School disaster management mirrors individual and family disaster prevention, and wider community disaster prevention efforts. This paper presents the usage of E-PreS System as a helpful, managerial tool during the school earthquake drill, in order to support schools in developing effective disaster and emergency plans specific to their local needs. The project comes up with a holistic methodology using real-time evaluation involving different categories of actors, districts, steps and metrics. The main outcomes of E-PreS project are the development of E-PreS web platform that host the needed data of school emergency planning; the development of E-PreS System; the implementation of disaster drills using E-PreS System in educational premises and local schools; and the evaluation of E-PreS System. Taking into consideration that every disaster drill aims to test and valid school plan and procedures; clarify and train personnel in roles and responsibilities; improve interagency coordination; identify gaps in resources; improve individual performance; and identify opportunities for improvement, E-PreS Project was submitted and approved by the European Commission (EC).

Keywords: Disaster drills, earthquake preparedness, E-PreS system, school emergency plans.

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Authors: Jung-Eun Park, Kyung-Whan Oh

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Intelligent systems are required in order to quickly and accurately analyze enormous quantities of data in the Internet environment. In intelligent systems, information extracting processes can be divided into supervised learning and unsupervised learning. This paper investigates intelligent clustering by unsupervised learning. Intelligent clustering is the clustering system which determines the clustering model for data analysis and evaluates results by itself. This system can make a clustering model more rapidly, objectively and accurately than an analyzer. The methodology for the automatic clustering intelligent system is a multi-agent system that comprises a clustering agent and a cluster performance evaluation agent. An agent exchanges information about clusters with another agent and the system determines the optimal cluster number through this information. Experiments using data sets in the UCI Machine Repository are performed in order to prove the validity of the system.

Keywords: Intelligent Clustering, Multi-Agent System, PCA, SOM, VC(Variance Criterion)

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Authors: Kazunori Nomura, Hiromichi Ogi, Masaumi Nakahara, Sou Watanabe, Atsuhiro Shibata

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Chemical Processing Facility of Japan Atomic Energy Agency is a basic research field for advanced back-end technology developments with using actual high-level radioactive materials such as irradiated fuels from the fast reactor, high-level liquid waste from reprocessing plant. In the nature of a research facility, various kinds of chemical reagents have been offered for fundamental tests. Most of them were treated properly and stored in the liquid waste vessel equipped in the facility, but some were not treated and remained at the experimental space as a kind of legacy waste. It is required to treat the waste in safety. On the other hand, we formulated the Medium- and Long-Term Management Plan of Japan Atomic Energy Agency Facilities. This comprehensive plan considers Chemical Processing Facility as one of the facilities to be decommissioned. Even if the plan is executed, treatment of the “legacy” waste beforehand must be a necessary step for decommissioning operation. Under this circumstance, we launched a collaborative research project called the STRAD project, which stands for Systematic Treatment of Radioactive liquid waste for Decommissioning, in order to develop the treatment processes for wastes of the nuclear research facility. In this project, decomposition methods of chemicals causing a troublesome phenomenon such as corrosion and explosion have been developed and there is a prospect of their decomposition in the facility by simple method. And solidification of aqueous or organic liquid wastes after the decomposition has been studied by adding cement or coagulants. Furthermore, we treated experimental tools of various materials with making an effort to stabilize and to compact them before the package into the waste container. It is expected to decrease the number of transportation of the solid waste and widen the operation space. Some achievements of these studies will be shown in this paper. The project is expected to contribute beneficial waste management outcome that can be shared world widely.

Keywords: Chemical Processing Facility, medium- and long-term management plan of JAEA Facilities, STRAD project, treatment of radioactive waste.

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Authors: Jolanta Tamošaitienė

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The article focuses on a developed comprehensive model to be used in an agile environment for the risk assessment and selection based on multi-attribute methods. The model is based on a multi-attribute evaluation of risk in construction, and the determination of their optimality criterion values are calculated using complex Multiple Criteria Decision-Making methods. The model may be further applied to risk assessment in an agile construction environment. The attributes of risk in a construction project are selected by applying the risk assessment condition to the construction sector, and the construction process efficiency in the construction industry accounts for the agile environment. The paper presents the comprehensive risk assessment model in an agile construction environment. It provides a background and a description of the proposed model and the developed analysis of the comprehensive risk assessment model in an agile construction environment with the criteria.

Keywords: Assessment, environment, agile, model, risk.

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Authors: Daniele Traversaro

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Problem: Senior citizens have been facing a challenging reality as a result of strict public health measures designed to protect people from the COVID-19 outbreak. These include the risk of social isolation due to the inability of the elderly to integrate with technology. Never before have Information and Communication Technology (ICT) skills become essential for their everyday life. Although third-age ICT education and lifelong learning are widely supported by universities and governments, there is a lack of literature on which teaching strategy/methodology to adopt in an entirely online ICT course aimed at third-age learners. This contribution aims to present an application of the Think-Pair-Share (TPS) learning method in an ICT third-age virtual classroom with an intergenerational approach to conducting online group labs and review activities. Research Question: Is collaborative learning suitable and effective, in terms of student engagement and learning outcomes, in an online ICT course for the elderly? Methods: In the TPS strategy a problem is posed by the teacher, students have time to think about it individually, and then they work in pairs (or small groups) to solve the problem and share their ideas with the entire class. We performed four experiments in the ICT course of the University of the Third Age of Genova (University of Genova, Italy) on the Microsoft Teams platform. The study cohort consisted of 26 students over the age of 45. Data were collected through online questionnaires. Two have been proposed, one at the end of the first activity and another at the end of the course. They consisted of five and three close-ended questions, respectively. The answers were on a Likert scale (from 1 to 4) except two questions (which asked the number of correct answers given individually and in groups) and the field for free comments/suggestions. Results: Groups achieve better results than individual students (with scores greater than one order of magnitude) and most students found TPS helpful to work in groups and interact with their peers. Insights: From these early results, it appears that TPS is suitable for an online third-age ICT classroom and useful for promoting discussion and active learning. Despite this, our work has several limitations. First of all, the results highlight the need for more data to be able to perform a statistical analysis in order to determine the effectiveness of this methodology in terms of student engagement and learning outcomes as future direction.

Keywords: Collaborative learning, information technology education, lifelong learning, older adult education, think-pair-share.

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Authors: Sobhy Fathy A. Hashesh

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This study aimed at investigating the effect of Andalus Knowledge Phases and Times (ANPT) model of learning and the effect of 'Intel Education Contribution in ANPT' on the development of students’ academic performance and emotional quotient. The society of the study composed of Andalus Private Schools, elementary school students (N=700), while the sample of the study composed of four randomly assigned groups (N=80) with one experimental group and one control group to study "ANPT" effect and the "Intel Contribution in ANPT" effect respectively. The study followed the quantitative and qualitative approaches in collecting and analyzing data to answer the study questions. Results of the study revealed that there were significant statistical differences between students’ academic performances and emotional quotients for the favor of the experimental groups. The study recommended applying this model on different educational variables and on other age groups to generate more data leading to more educational results for the favor of students’ learning outcomes.

Keywords: ANPT, Flipped Classroom, 5Es learning Model, Kagan structures.

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: Luisa Mesquita Oliveira Ribeiro, Alexei Manso Correa Machado

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Handwritten signature is a unique form for recognizing an individual, used to discern documents, carry out investigations in the criminal, legal, banking areas and other applications. Signature verification is based on large amounts of biometric data, as they are simple and easy to acquire, among other characteristics. Given this scenario, signature forgery is a worldwide recurring problem and fast and precise techniques are needed to prevent crimes of this nature from occurring. This article carried out a study on the efficiency of the Capsule Network in analyzing and recognizing signatures. The chosen architecture achieved an accuracy of 98.11% and 80.15% for the CEDAR and GPDS databases, respectively.

Keywords: Biometrics, deep learning, handwriting, signature forgery.

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Authors: Amah Nnachi Lofty, Oyefeso Olufemi, Ibiam Udu Ama

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New and more powerful communications technologies continue to emerge at a rapid pace and their uses in education are widespread and the impact remarkable in the developing societies. This study investigates Mobile Collaboration Learning Technique (MCLT) on learners’ outcome among students in tertiary institutions of developing nations (a case of Nigeria students). It examines the significance of retention achievement scores of students taught using mobile collaboration and conventional method. The sample consisted of 120 students using Stratified random sampling method. Five research questions and hypotheses were formulated, and tested at 0.05 level of significance. A student achievement test (SAT) was made of 40 items of multiple-choice objective type, developed and validated for data collection by professionals. The SAT was administered to students as pre-test and post-test. The data were analyzed using t-test statistic to test the hypotheses. The result indicated that students taught using MCLT performed significantly better than their counterparts using the conventional method of instruction. Also, there was no significant difference in the post-test performance scores of male and female students taught using MCLT. Based on the findings, the following submissions was made that: Mobile collaboration system be encouraged in the institutions to boost knowledge sharing among learners, workshop and training should be organized to train teachers on the use of this technique, schools and government should consistently align curriculum standard to trends of technological dictates and formulate policies and procedures towards responsible use of MCLT.

Keywords: Education, communication, learning, mobile collaboration, technology.

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of Machine Learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. Artificial Intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and two defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt ML techniques without rigorous testing, since they may be vulnerable to adversarial attacks, especially in security-critical areas such as the nuclear industry. We observed that while the adopted defence methods can effectively defend against different attacks, none of them could protect against all five adversarial attacks entirely.

Keywords: Resilient Machine Learning, attacks, defences, nuclear industry, crack detection.

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Authors: Sophia Hayes, Kenny L. Liang, Sahil Sharma, Austin Shema, Mahmoud Bader, Mohamed Elbarkouky

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Despite being one of the most significant economic contributors of the country, Canada’s construction industry is lagging behind other sectors when it comes to labor productivity improvements. The construction industry is very collaborative as a general contractor, will hire trade contractors to perform most of a project’s work; meaning low productivity from one contractor can have a domino effect on the shared success of a project. To address this issue and encourage trade contractors to improve their productivity tracking methods, an investigative study was done on the productivity views and tracking methods of various trade contractors. Additionally, an in-depth review was done on four standard tracking methods used in the construction industry: cost codes, benchmarking, the job productivity measurement (JPM) standard, and WorkFace Planning (WFP). The four tracking methods were used as a baseline in comparing the trade contractors’ responses, determining gaps within their current tracking methods, and for making improvement recommendations. 15 interviews were conducted with different trades to analyze how contractors value productivity. The results of these analyses indicated that there seem to be gaps within the construction industry when it comes to an understanding of the purpose and value in productivity tracking. The trade contractors also shared their current productivity tracking systems; which were then compared to the four standard tracking methods used in the construction industry. Gaps were identified in their various tracking methods and using a framework; recommendations were made based on the type of trade on how to improve how they track productivity.

Keywords: Trade contractors’ productivity, productivity tracking, cost codes, benchmarking, job productivity measurement, JPM, workface planning WFP.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: Metagenomics, phenotype prediction, deep learning, embeddings, multiple instance learning.

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Authors: Lida Haghnegahdar, Ezzatollah Asgharizadeh

Abstract:

Value engineering is an efficacious contraption for administrators to make up their minds. Value perusals proffer the gaffers a suitable instrument to decrease the expenditures of the life span, quality amelioration, structural improvement, curtailment of the construction schedule, longevity prolongation or a merging of the aforementioned cases. Subjecting organizers to pressures on one hand and their accountability towards their pertinent fields together with inherent risks and ambiguities of other options on the other hand set some comptrollers in a dilemma utilization of risk management and the value engineering in projects manipulation with regard to complexities of implementing projects can be wielded as a contraption to identify and efface each item which wreaks unnecessary expenses and time squandering sans inflicting any damages upon the essential project applications. Of course It should be noted that implementation of risk management and value engineering with regard to the betterment of efficiency and functions may lead to the project implementation timing elongation. Here time revamping does not refer to time diminishing in the whole cases. his article deals with risk and value engineering conceptualizations at first. The germane reverberations effectuated due to its execution in Iran Khodro Corporation are regarded together with the joint features and amalgamation of the aforesaid entia; hence the proposed blueprint is submitted to be taken advantage of in engineering and industrial projects including Iran Khodro Corporation.

Keywords: Management, risk engineering, value engineering, project manipulation, Iran Khodro.

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Authors: Wei Bin Shen

Abstract:

Lou Gang world irrigation project heritage in Taihu Lake is a systematic irrigation project integrating water conservancy, ecology and culture. Through the methods of historical documents and field investigation, this paper deeply analyzes the formation history, connotation and value of Lou Gang polder system: Lou Gang heritage, describes in detail the relationship between Lou Gang polder system in Taihu Lake and the development and evolution of Huzhou City, and initially explores the protection and Utilization Strategies of Lou Gang water conservancy cultural heritage resources in Taihu Lake from the current situation.

Keywords: Lou Gang, protection strategy, urban evolution, water conservancy cultural heritage.

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Authors: Hager Kammoun, Jean Charles Lamirel, Mohamed Ben Ahmed

Abstract:

In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term" knowledge about past queries and concepts in a collection of documents. The “long term" knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.

Keywords: Information Retrieval Systems, machine learning, classification, Galois lattices, Self Organizing Map.

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