Search results for: network user rules

Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Shilan Rahmani Azr, Siavash Emtiyaz

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Recent development in computing and communicative technologies leads to much easier mobile accessibility to the information. Users can access to the information in different places using various deceives in which the care variety of abilities. Meanwhile, the format and details of electronic documents are changing each day. In these cases, a mismatch is created between content and client’s abilities. Recently the service-oriented content adaption has been developed which the adapting tasks are dedicated to some extended services. In this method, the main problem is to choose the best appropriate service among accessible and distributed services. In this paper, a method for determining the optimal path to the best services, based on the quality control parameters and user preferences, is proposed using max heap tree. The efficiency of this method in contrast to the other previous methods of the content adaptation is related to the determining the optimal path of the best services which are measured. The results show the advantages and progresses of this method in compare of the others.

Keywords: service-oriented content adaption, QoS, max heap tree, web services

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Authors: Danjuma Bawa

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This paper aimed at exploring the capabilities of Location-Allocation model in complementing the strides of the existing physical planning models in the location and distribution of facilities for regional consumption. The paper was designed to provide a blueprint to the Nigerian government and other donor agencies especially the Fertilizer Distribution Initiative (FDI) by the federal government for the revitalization of the terrorism ravaged regions. Theoretical underpinnings of central place theory related to spatial distribution, interrelationships, and threshold prerequisites were reviewed. The study showcased how Location-Allocation Model (L-AM) alongside Central Place Theory (CPT) was applied in Geographic Information System (GIS) environment to; map and analyze the spatial distribution of settlements; exploit their physical and economic interrelationships, and to explore their hierarchical and opportunistic influences. The study was purely spatial qualitative research which largely used secondary data such as; spatial location and distribution of settlements, population figures of settlements, network of roads linking them and other landform features. These were sourced from government ministries and open source consortium. GIS was used as a tool for processing and analyzing such spatial features within the dictum of CPT and L-AM to produce a comprehensive spatial digital plan for equitable and judicious location and distribution of fertilizer deports in the study area in an optimal way. Population threshold was used as yardstick for selecting suitable settlements that could stand as service centers to other hinterlands; this was accomplished using the query syntax in ArcMapTM. ArcGISTM’ network analyst was used in conducting location-allocation analysis for apportioning of groups of settlements around such service centers within a given threshold distance. Most of the techniques and models ever used by utility planners have been centered on straight distance to settlements using Euclidean distances. Such models neglect impedance cutoffs and the routing capabilities of networks. CPT and L-AM take into consideration both the influential characteristics of settlements and their routing connectivity. The study was undertaken in two terrorism ravaged Local Government Areas of Adamawa state. Four (4) existing depots in the study area were identified. 20 more depots in 20 villages were proposed using suitability analysis. Out of the 300 settlements mapped in the study area about 280 of such settlements where optimally grouped and allocated to the selected service centers respectfully within 2km impedance cutoff. This study complements the giant strides by the federal government of Nigeria by providing a blueprint for ensuring proper distribution of these public goods in the spirit of bringing succor to these terrorism ravaged populace. This will ardently at the same time help in boosting agricultural activities thereby lowering food shortage and raising per capita income as espoused by the government.

Keywords: central place theory, GIS, location-allocation, network analysis, urban and regional planning, welfare economics

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Authors: Keshab Shrestha, Hung-Suck Park

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Emergy accounting of the systems networks is guided by a definite rule called ‘emergy algebra’. The systems networks consist of two types of branching. These are the co-product branching and split branching. The emergy accounting procedure for both the branching types is different. According to the emergy algebra, each branch in the co-product branching has different transformity values whereas the split branching has the same transformity value. After the transformity value of each branch is determined, the emergy is calculated by multiplying this with the energy. The aim of this research is to solve the problems in determining the transformity values in the co-product branching through the introduction of a new methodology, the modified physical quantity method. Initially, the existing methodologies for emergy accounting in the co-product branching is discussed and later, the modified physical quantity method is introduced with a case study of the Eucalyptus pulp production. The existing emergy accounting methodologies in the co-product branching has wrong interpretations with incorrect emergy calculations. The modified physical quantity method solves those problems of emergy accounting in the co-product branching systems. The transformity value calculated for each branch is different and also applicable in the emergy calculations. The methodology also strictly follows the emergy algebra rules. This new modified physical quantity methodology is a valid approach in emergy accounting particularly in the multi-production systems networks.

Keywords: co-product branching, emergy accounting, emergy algebra, modified physical quantity method, transformity value

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Leandros A. Maglaras, Jianmin Jiang

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The protection of the national infrastructures from cyberattacks is one of the main issues for national and international security. The funded European Framework-7 (FP7) research project CockpitCI introduces intelligent intrusion detection, analysis and protection techniques for Critical Infrastructures (CI). The paradox is that CIs massively rely on the newest interconnected and vulnerable Information and Communication Technology (ICT), whilst the control equipment, legacy software/hardware, is typically old. Such a combination of factors may lead to very dangerous situations, exposing systems to a wide variety of attacks. To overcome such threats, the CockpitCI project combines machine learning techniques with ICT technologies to produce advanced intrusion detection, analysis and reaction tools to provide intelligence to field equipment. This will allow the field equipment to perform local decisions in order to self-identify and self-react to abnormal situations introduced by cyberattacks. In this paper, an intrusion detection module capable of detecting malicious network traffic in a Supervisory Control and Data Acquisition (SCADA) system is presented. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM is an intrusion detection mechanism that does not need any labeled data for training or any information about the kind of anomaly is expecting for the detection process. This feature makes it ideal for processing SCADA environment data and automates SCADA performance monitoring. The OCSVM module developed is trained by network traces off line and detects anomalies in the system real time. The module is part of an IDS (intrusion detection system) developed under CockpitCI project and communicates with the other parts of the system by the exchange of IDMEF messages that carry information about the source of the incident, the time and a classification of the alarm.

Keywords: cyber-security, SCADA systems, OCSVM, intrusion detection

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Shu-Lu Hsu, Fang-Yi Chu

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With the advances in information and communications technology, mobile context-aware applications have become powerful marketing tools. In Apple online store, there are numerous mobile applications (APPs) developed for destination tour. This study investigated the determinants of adoption of context-aware APPs for destination tour services. A model is proposed based on Technology Acceptance Model and privacy concern theory. The model was empirically tested based on a sample of 259 users of a tourism APP published by Kaohsiung Tourism Bureau, Taiwan. The results showed that the fitness of the model is well and, among all the factors, the perceived usefulness and perceived ease of use have the most significant influences on the intention to adopt context-aware destination APPs. Finally, contrary to the findings of previous literature, the effect of privacy concern on the adoption intention of context-aware APP is insignificant.

Keywords: mobile application, context-aware, privacy concern, TAM

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Authors: Andrea Ghermandi

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Natural treatment systems such as constructed wetlands and waste stabilization ponds are increasingly used to treat water and wastewater from a variety of sources, including stormwater and polluted surface water. The provision of ancillary benefits in the form of cultural ecosystem services makes these systems unique among water and wastewater treatment technologies and greatly contributes to determine their potential role in promoting sustainable water management practices. A quantitative analysis of these benefits, however, has been lacking in the literature. Here, a critical assessment of the recreational and educational benefits in natural treatment systems is provided, which combines observed public use from a survey of managers and operators with estimated public use as obtained using geotagged photos from social media as a proxy for visitation rates. Geographic Information Systems (GIS) are used to characterize the spatial boundaries of 273 natural treatment systems worldwide. Such boundaries are used as input for the Application Program Interfaces (APIs) of two popular photo-sharing websites (Flickr and Panoramio) in order to derive the number of photo-user-days, i.e., the number of yearly visits by individual photo users in each site. The adequateness and predictive power of four univariate calibration models using the crowdsourced data as a proxy for visitation are evaluated. A high correlation is found between photo-user-days and observed annual visitors (Pearson's r = 0.811; p-value < 0.001; N = 62). Standardized Major Axis (SMA) regression is found to outperform Ordinary Least Squares regression and count data models in terms of predictive power insofar as standard verification statistics – such as the root mean square error of prediction (RMSEP), the mean absolute error of prediction (MAEP), the reduction of error (RE), and the coefficient of efficiency (CE) – are concerned. The SMA regression model is used to estimate the intensity of public use in all 273 natural treatment systems. System type, influent water quality, and area are found to statistically affect public use, consistently with a priori expectations. Publicly available information regarding the home location of the sampled visitors is derived from their social media profiles and used to infer the distance they are willing to travel to visit the natural treatment systems in the database. Such information is analyzed using the travel cost method to derive monetary estimates of the recreational benefits of the investigated natural treatment systems. Overall, the findings confirm the opportunities arising from an integrated design and management of natural treatment systems, which combines the objectives of water quality enhancement and provision of cultural ecosystem services through public use in a multi-functional approach and compatibly with the need to protect public health.

Keywords: constructed wetlands, cultural ecosystem services, ecological engineering, waste stabilization ponds

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Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

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Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: aerodynamic angles, air data system, flight test, neural network, unmanned aerial vehicle, virtual sensor

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Authors: Shuqi Zhang

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Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Jiří Barta, Tomáš Ludík, Jiří Urbánek

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The topics of disaster and emergency management are highly debated among experts. Fast communication will help to deal with emergencies. Problem is with the network connection and data exchange. The paper suggests a solution, which allows possibilities and perspectives of new flexible communication platform to the protection of communication systems for crisis management. This platform is used for everyday communication and communication in crisis situations too.

Keywords: crisis management, information systems, interoperability, crisis communication, security environment, communication platform

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Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: Kawai Nobuaki

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This paper describes the design and implementation of web system for continuable and viable collaboration. This study proposes the improvement of the system based on a result of a certain practice. As contemporary higher education information environments transform, this study highlights the significance of university identity and college identity that are formed continuously through independent activities of the students. Based on these discussions, the present study proposes a practical media environment design which facilitates the processes of organizational identity formation based on a continuous and cyclical model. Even if users change by this system, the communication system continues operation and cooperation. The activity becomes the archive and produces new activity. Based on the result, this study elaborates a plan with a re-design by a system from the viewpoint of second-order cybernetics. Systems theory is a theoretical foundation for our study.

Keywords: collaborative work, learning management system, second-order cybernetics, systems theory, user generated contents, viable system model

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Authors: Hong Yu, Dirk Heider, Suresh Advani

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Increasing demands for high strength and lightweight materials in aircraft industry prompted the wide use of carbon composites in recent decades. Carbon composite laminates used on aircraft structures are subject to lightning strikes. Unlike its metal/alloy counterparts, carbon fiber reinforced composites demonstrate smaller electrical conductivity, yielding more severe damages due to Joule heating. The anisotropic nature of composite laminates makes the electrical and thermal conduction within carbon composite laminates even more complicated. Good understanding of the electrical conduction behavior of carbon composites is the key to effective lightning protection design. The goal of this study is to numerically and experimentally investigate the impact of ultra-high temperature induced by simulated lightning strike on the electrical conduction of carbon composites. A lightning simulator is designed to apply standard lightning current waveform to composite laminates. Multiple carbon composite laminates made from IM7 and AS4 carbon fiber are tested and the transient resistance data is recorded. A microstructure based resistor network model is developed to describe the electrical and thermal conduction behavior, with consideration of temperature dependent material properties. Material degradations such as thermal and electrical breakdown are also modeled to include the effect of high current and high temperature induced by lightning strikes. Good match between the simulation results and experimental data indicates that the developed model captures the major conduction mechanisms. A parametric study is then conducted using the validated model to investigate the effect of system parameters such as fiber volume fraction, inter-ply interface quality, and lightning current waveforms.

Keywords: carbon composite, joule heating, lightning strike, resistor network

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Authors: Suphitcha Charoen-Amornkitt, Chokeanand Bussracumpakorn

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The tendency of the Karen backstrap weaving succession has gradually decreased due to the difficulty of weaving techniques and the relocation of the young generation. The Yang Nam Klat Nuea community, Nong Ya Plong District, Phetchaburi, is a Pwo Karen community that is seriously confronted with a lack of cultural heritage. Thus, a group of weavers was formed to revive the knowledge of weaving. However, they have been gradually confronted with culture assimilation to mainstream culture from the desire for marketing acceptance and imperative and forced the extinction of culture due to the disappearance of weaving details and techniques. Although there are practical solutions, i.e., product development, community improvement, knowledge improvement, and knowledge transfer, to inherit the Karen weaving culture, people in the community cannot fulfill their deep intention about the weaving inheritance as most solutions have focused on developing the commercial products and making the income instead of inheriting their knowledge. This research employed qualitative user research with an in-depth user interview to study communal knowledge transfer succession based on the internal involved parties, i.e., four expert weavers, three young weavers, and three 4th generation villagers. The purpose is to explore the correlation and mindset of villagers towards the culture with specific issues, including the psychology of culture, core knowledge and learning methods, cultural inheritance, and cultural engagement. As a result, the existing models of knowledge management mostly focused on tangible strategies, which can notice progress in short terms, such as direct teaching and consistent practicing. At the same time, the motivation and passion of inheritors were abolished while the research found that the young generation who profoundly connected with the textile culture will have a more significant intention to continue the culture. Therefore, this research suggests both internal and external solutions to treat the community. Regarding the internal solutions, family, weaving group, and school have an important role to participate with young villagers by encouraging activities to support the cultivating of Karen’s history, understanding their identities, and adapting the culture as a part of daily life. At the same time, collecting all of the knowledge in the archives, e.g., recorded video, instruction, and books, can crucially prevent the culture from extinction. Regarding the external solutions, this study suggests that working with social media will enhance the intimacy of textile culture, while the community should relieve the roles in marketing competition and start to drive cultural experiences to create a new market position. In conclusion, this research intends to explore the causes and motivation to support the transfer of the culture to the 4th generation villagers and to raise awareness of the diversity of culture in society. With these suggestions and the desire to improve pride and confidence in culture, the community agrees that strengthening the relationships between the young villagers and the weaving culture can bring attention and interest back to the weaving culture.

Keywords: Pwo Karen textile culture, backstrap weaving succession, cultural inheritance, knowledge transfer, knowledge management

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Authors: H.-H. Doh, J.-M. Yu, Y.-J. Kwon, J.-H. Shin, H.-W. Kim, S.-H. Nam, D.-H. Lee

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This paper suggests a decision tree based approach for flexible job shop scheduling with multiple process plans, i. e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decision variables are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. As an extension of the priority scheduling approach that selects the best priority rule combination after many simulation runs, this study suggests a decision tree based approach in which a decision tree is used to select a priority rule combination adequate for a specific system state and hence the burdens required for developing simulation models and carrying out simulation runs can be eliminated. The decision tree based scheduling approach consists of construction and scheduling modules. In the construction module, a decision tree is constructed using a four-stage algorithm, and in the scheduling module, a priority rule combination is selected using the decision tree. To show the performance of the decision tree based approach suggested in this study, a case study was done on a flexible job shop with reconfigurable manufacturing cells and a conventional job shop, and the results are reported by comparing it with individual priority rule combinations for the objectives of minimizing total flow time and total tardiness.

Keywords: flexible job shop scheduling, decision tree, priority rules, case study

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Authors: Felix Böck

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Nowadays, data about learners and their digital activities are collected, which could help educational institutions to better understand learning processes, improve them and be able to provide better learning assistance. In this research project, custom knowledge- and data-driven recommendation algorithms will be used to offer students in higher education integrated learning assistance. The pre-requisite for this is a learner model that is as comprehensive as possible, which should first be created and then kept up-to-date largely automatically for being able to individualize and personalize the learning experience. In order to create such a learner model, a roadmap is presented that describes the individual phases up to the creation and evaluation of the finished model. The methodological process for the research project is disclosed, and the research question of how learners can be supported in their learning with personalized, customized learning recommendations is explored.

Keywords: research agenda, user model, learner model, higher education, adaptive educational digital learning environments, personalized learning paths, recommendation system, adaptation, personalization

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Authors: Swee Yang Low, Dong Eon Kim, Canh Tien Trinh Nguyen, Yixiong Cai, Shie-Yui Liong

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Nee Soon swamp forest is one of the last vestiges of tropical wetland in Singapore. Understanding the hydrological regime of the swamp forest and implications for water quality is critical to guide stakeholders in implementing effective measures to preserve the wetland against anthropogenic impacts. In particular, although current field measurement data do not indicate a concern with organic pollution, reviewing the ways in which the wetland responds to elevated organic waste influx (and the corresponding impact on dissolved oxygen, DO) can help identify potential hotspots, and the impact on the outflow from the catchment which drains into downstream controlled watercourses. An integrated water quality model is therefore developed in this study to investigate spatial and temporal concentrations of DO levels and organic pollution (as quantified by biochemical oxygen demand, BOD) within the catchment’s river network under hypothetical, projected scenarios of spiked upstream inflow. The model was developed using MIKE HYDRO for modelling the study domain, as well as the MIKE ECO Lab numerical laboratory for characterising water quality processes. Model parameters are calibrated against time series of observed discharges at three measurement stations along the river network. Over a simulation period of April 2014 to December 2015, the calibrated model predicted that a continuous spiked inflow of 400 mg/l BOD will elevate downstream concentrations at the catchment outlet to an average of 12 mg/l, from an assumed nominal baseline BOD of 1 mg/l. Levels of DO were decreased from an initial 5 mg/l to 0.4 mg/l. Though a scenario of spiked organic influx at the swamp forest’s undeveloped upstream sub-catchments is currently unlikely to occur, the outcomes nevertheless will be beneficial for future planning studies in understanding how the water quality of the catchment will be impacted should urban redevelopment works be considered around the swamp forest.

Keywords: hydrology, modeling, water quality, wetland

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Authors: Johanna Renny Octavia, Meliana Nurdin, Ignatius Kevin Kurniawan, Ricca Aksara

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It is widely believed that usability evaluation is necessary to evaluate a website design for further improvement. Traditional methods of usability evaluation have given sufficient insights to reveal usability problems of websites. Eye-tracking analysis has been considered as a useful method that adds a powerful dimension to web usability evaluation. It allows web designers and usability researchers to understand exactly what users do and do not see on a web page, thus disclose more information on web usability and provide a more complete insights on a website design. This paper elaborates on moving beyond traditional methods of web usability evaluation by integrating eye-tracking analysis to enhance the evaluation of website design, and presents three case studies to support this approach. In these case studies, eye movement metrics such as gaze plots and fixation-derived metrics, and user performance data such as task completion times and number of errors were recorded as objective measurements that can inform the necessity for website design improvements.

Keywords: design, eye-tracking, usability evaluation, website

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Authors: Xiaoyu Stephanie Ren

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With the increase of massive migration, economic polarization, as well as increasing awareness and respects for religious freedom in the world, women have become unprecedentedly vulnerable to trafficking involving religious coercion. Such cases can also bring enormous challenges for prosecution in which the prosecutor bears the burden of proving that the victim acted, or not acted in a certain way due to the exploitation of her belief system: (1) Jurors who are nonbelievers tend not to be convinced that something of intangible nature can act as the force to get victim into women trafficking situation; (2) Court more often than not rules in favor of victims in women trafficking cases involving religious exploitation only when there is physical coercion in addition to religious coercion; (3) Female victims are often reluctant to testify at court due to their godly fear and loyalty to trafficker. Using case study methodology, this paper examines the unique characteristics of religious coercion as means of trafficking in women from a legal perspective and proposes multiple ways based on communal beliefs that faith communities, as victims for such crime themselves, can act in order to help to end religious exploitation. The purpose of this paper is threefold: to improve acknowledgment for the role of religious coercion as a sole force for women trafficking situation; to discuss legal hurdles in prosecuting women trafficking cases involving religious coercion; and to propose collaboration across borders among faith communities to end such exploitation.

Keywords: women trafficking, sex violence, religious exploitation, faith community, prosecution, law

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Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Ebenezer Joseph, Veena Easvaradoss, S. Sundar Manoharan, David Chandran, Sumathi Chandrasekaran, T. R. Uma

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Perceptual reasoning is the ability that incorporates fluid reasoning, spatial processing, and visual motor integration. Several theories of cognitive functioning emphasize the importance of fluid reasoning. The ability to manipulate abstractions and rules and to generalize is required for reasoning tasks. This study, funded by the Cognitive Science Research Initiative, Department of Science and Technology, Government of India, analyzed the effect of 1-year chess training on the perceptual reasoning of children. A pretest–posttest with control group design was used, with 43 (28 boys, 15 girls) children in the experimental group and 42 (26 boys, 16 girls) children in the control group. The sample was selected from children studying in two private schools from South India (grades 3 to 9), which included both the genders. The experimental group underwent weekly 1-hour chess training for 1 year. Perceptual reasoning was measured by three subtests of WISC-IV INDIA. Pre-equivalence of means was established. Further statistical analyses revealed that the experimental group had shown statistically significant improvement in perceptual reasoning compared to the control group. The present study clearly establishes a correlation between chess learning and perceptual reasoning. If perceptual reasoning can be enhanced in children, it could possibly result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess, cognition, intelligence, perceptual reasoning

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Authors: Orestis Kasinopoulos, Maria Karekla, Vasilis Vasiliou, Evangelos Karademas

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Acceptance and Commitment Therapy (ACT) is an empirically supported intervention for treating Chronic Pain Patients, yet its effectiveness for some chronic conditions or when adapted to other languages, has not been explored. An ACT group intervention was designed to explore the effectiveness of treating a Greek speaking heterogeneous sample of Chronic Pain patients with the aim of increasing quality of life, acceptance of pain and functionality. Sixty-nine patients were assessed and randomly assigned to an ACT or control group (relaxation techniques) for eight, 90-minute, sessions. Results are currently being analysed and follow-ups (6 and 12 month) are being completed. Low adherence rates and high attrition rates observed in the study, however point to the direction of future modified interventions. Such modifications may include web-based and smartphone interventions and their benefits in being implemented in chronic pain patients.

Keywords: chronic pain, ACT, internet-delivered, digitalised intervention, adherence, attrition

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

Procedia PDF Downloads 107

Authors: Geremew Geidare Kailo, Igor Gáspár, András Koris, Ivana Pajčin, Flóra Vitális, Vanja Vlajkov

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Three-dimension (3D) printing, a very popular additive manufacturing technology, has recently undergone rapid growth and replaced the use of conventional technology from prototyping to producing end-user parts and products. The 3D Printing technology involves a digital manufacturing machine that produces three-dimensional objects according to designs created by the user via 3D modeling or computer-aided design/manufacturing (CAD/CAM) software. The most popular 3D printing system is Fused Deposition Modeling (FDM) or also called Fused Filament Fabrication (FFF). A 3D-printed object is considered food safe if it can have direct contact with the food without any toxic effects, even after cleaning, storing, and reusing the object. This work analyzes the processing timeline of the filament (material for 3D printing) from unboxing to the extrusion through the nozzle. It is an important task to analyze the growth of bacteria on the 3D printed surface and in gaps between the layers. By default, the 3D-printed object is not food safe after longer usage and direct contact with food (even though they use food-safe filaments), but there are solutions for this problem. The aim of this work was to evaluate the 3D-printed object from different perspectives of food safety. Firstly, testing antimicrobial 3D printing filaments from a food safety aspect since the 3D Printed object in the food industry may have direct contact with the food. Therefore, the main purpose of the work is to reduce the microbial load on the surface of a 3D-printed part. Coating with epoxy resin was investigated, too, to see its effect on mechanical strength, thermal resistance, surface smoothness and food safety (cleanability). Another aim of this study was to test new temperature-resistant filaments and the effect of high temperature on 3D printed materials to see if they can be cleaned with boiling or similar hi-temp treatment. This work proved that all three mentioned methods could improve the food safety of the 3D printed object, but the size of this effect variates. The best result we got was with coating with epoxy resin, and the object was cleanable like any other injection molded plastic object with a smooth surface. Very good results we got by boiling the objects, and it is good to see that nowadays, more and more special filaments have a food-safe certificate and can withstand boiling temperatures too. Using antibacterial filaments reduced bacterial colonies to 1/5, but the biggest advantage of this method is that it doesn’t require any post-processing. The object is ready out of the 3D printer. Acknowledgements: The research was supported by the Hungarian and Serbian bilateral scientific and technological cooperation project funded by the Hungarian National Office for Research, Development and Innovation (NKFI, 2019-2.1.11-TÉT-2020-00249) and the Ministry of Education, Science and Technological Development of the Republic of Serbia. The authors acknowledge the Hungarian University of Agriculture and Life Sciences’s Doctoral School of Food Science for the support in this study

Keywords: food safety, 3D printing, filaments, microbial, temperature

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Authors: Elmahdi Mohammadine, Ahouzi Esmail, Najid Abdellah

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This contribution aims to present a new protected hybrid WDM/TDM PON architecture using Wavelength Selective Switches and Optical Line Protection devices. The objective from using these technologies is to improve flexibility and enhance the protection of GPON networks.

Keywords: Wavlenght Division Multiplexed Passive Optical Network (WDM-PON), Time Division Multiplexed PON (TDM-PON), architecture, Protection, Wavelength Selective Switches (WSS), Optical Line Protection (OLP)

Procedia PDF Downloads 542