ULAM Headless Course Format

Current e-learning software comes with a huge technological debt and does not respond to market needs as fast as other IT segments can. The main reason is dependency on obsolete formats like SCORM that are still widely used, and which do not separate data layer from the presentation layer. There is a need from market for existence of better designed and better implemented formats.

Enhancing Academic Advising In Credit Hours System Using Dss

Academic advising plays a vital role in achieving higher educational institution’s purposes. Academic advising is a process where an academic advisor decides to select a certain number of courses for a student to register in each semester to fulfil the graduation requirements. This paper presents an Academic Advising Decision Support System (AADSS) to enhance advisors make better decisions regarding their students’ cases. AADSS framework divided into four layers, data preparation layer, data layer, processing layer and decision layer. The testing results from those participating academic advisors and students considered are that AADSS beneficial in enhancing their decision for selecting courses.

Ethereum and IOTA based Battery Management System with Internet of Vehicles

The era of Electric Vehicles (EVs) has influenced the very make and manufacture of vehicles resulting in low pollution and advanced battery life. On the other hand, the internet of things has also expanded allowing a number of devices to stay connected using the internet. Massive drawbacks faced by EVs today are the limitation in battery swapping and charging stations and limitation in the range of batteries used. This proposed paper aims to efficiently manage the best battery system apart from building the essential infrastructure. In some cases battery swapping option is also provided through other EV drivers or at registered stations. Hence a complete database of the EV network is required so that it is possible to swap and charge batteries successfully. An EV management using two blockchains as a data layer and network of the application is implemented in this work. The first step involves the development of a blockchain framework using Ethereum and the next step entails a direct acyclic graph. When integrated, these two methodologies prove to be an efficient platform that offers a viable solution for battery management in Electric Vehicles.

Developing a Decision Support System for Water Resources Dispatching

Contradiction between water demand and water supply have a huge impact on social and economic development. This paper presents the development of a water resources dispatch decision support system. The system integrates models related to water dispatch such as streamflow forecast model, water allocation model and water dispatch model. Each model runs as an independent service and is registered in the model platform. The model platform interacts with the service layer and data layer through the model adapter. The model adapter is designed for converting the model input data sent by the service layer and the basic data and observation data queried by the data layer into the format required by the model. In case study, we took the Fu River Basin as an example to demonstrate an application of the system. The system realizes the complete process of data collection, streamflow forecast, water demand declaration, water distribution and water dispatch. User can get the recommended operation plan of the reservoir and the corresponding water supply result through the user interface. Process variables can also be viewed through the system, such as streamflow forecast results and water distribution results, etc. The proposed system can provide technical support and assistance for the decision makers, which also provide an effective demonstration for water resources management in other rivers.

An Architectural Framework for Healthcare Dashboards Design

In today’s competitive environment, one of the new tools in the field of information technology is business or organizational dashboards that are a backup in the process of strategic management of organizations. The purpose of the current research is to provide a framework to design the healthcare dashboard through technical architecture with fulfilling the decision-makers’ requirements. In this study, a common qualitative research method, metasynthesis, is applied, including a seven-step set of research questions, conducting systematic literature search and selection of suitable papers, data extraction, analysis and findings of the qualitative composition, quality control, and presentation of findings. During this process, 102 articles were found by saturation of information resources and then 12 articles were selected for extracting data using acceptance and rejection criteria. A critical evaluation method was used to evaluate the quality of selected articles. After investigating the selected articles and scoring them, in terms of quality, one article was very good, 10 articles were good, and one article was moderate. Then, with regard to the principles and guidelines of technical architecture, the required information was extracted from the selected articles and was analyzed with the method of open, axial, and selective coding. Following the steps of metasynthesis methods, the principles extracted with major and minor titles principles and guidelines in the form of multilayered system architecture including presentation layer, application layer, data layer, and technical infrastructure layer were classified. In the obtained framework, 15 indicators as the main principles and 66 subcriteria as the subsidiary principles for the design and technical architecture of enterprise dashboards were identified.

Bilayer Markov Random Field Method for Detecting Defects in Patterned Fabric

Existing block-level defect detection method in patterned fabric causes a large number of false detections due to the lack of edge information. To solve this problem, in this paper, we propose a bilayer Markov random field (BMRF) method for inspecting defects in patterned fabric. First, the proposed method reduces samples of the original fabric image to obtain the constraint layer, which can locate the defective block roughly. Second, we interpolate samples into the image to supplement the local information to improve and optimize the imperfect boundary, to obtain a more detailed data layer. Moreover, this paper proposes a new potential function, which considers the differential characteristics of the image blocks in the same layer and the transition probability between different layers. Finally, this paper utilizes a parameter estimation method based on the expectation maximization to solve the parameters of the BMRF method. The proposed BMRF method is evaluated on databases of star-, box- and dot-patterned fabrics. By comparing the resultant and ground-truth images, the recall rate of the proposed method in the three patterned fabrics is 95.32%, 89.29% and 93.28%, respectively, which is comparable to the existing methods.

A Method for Constructing Geographical Knowledge Graph from Multisource Data

Global problems all occur at a particular location on or near the Earth’s surface. Sitting at the junction of artificial intelligence (AI) and big data, knowledge graphs (KGs) organize, interlink, and create semantic knowledge, thus attracting much attention worldwide. Although the existing KGs are constructed from internet encyclopedias and contain abundant knowledge, they lack exact coordinates and geographical relationships. In light of this, a geographical knowledge graph (GeoKG) construction method based on multisource data is proposed, consisting of a modeling schema layer and a filling data layer. This method has two advantages: (1) the knowledge can be extracted from geographic datasets; (2) the knowledge on multisource data can be represented and integrated. Firstly, the schema layer is designed to represent geographical knowledge. Then, the methods of extraction and integration from multisource data are designed to fill the data layer, and a storage method is developed to associate semantics with geospatial knowledge. Finally, the GeoKG is verified through linkage rate, semantic relationship rate, and application cases. The experiments indicate that the method could automatically extract and integrate knowledge from multisource data. Additionally, our GeoKG has a higher success rate of linking web pages with geographic datasets, and its exact coordinates have increased to 100%. This paper could bridge the distance between a Geographic Information System and a KG, thus facilitating more geospatial applications.

Integration of land cadastre with spatial plan data

Establishing a multi-purpose cadastre, especially in terms of upgrading cadastral contents with the various spatial data, such as land use, is a challenge in Slovenia and internationally. Land use strongly affects spatial planning, development, and management, so high-quality spatial integration of the land cadastre with spatial plans data is crucial for effective land management. In the first part of the article, we reviewed the literature and documents that prescribe guidelines for the development of the land cadastre; we use these guidelines as a basis for developing a proposed method of linking and harmonising the data of the land cadastre with the spatial plan data. Land use is specified in spatial plans, and we linked it to the graphical and attribute land cadastre data layer. We tested the method in selected study areas in Prekmurje with a high-quality cadastre in the municipalities of Kramarovci and Nemčavci. As a result, we presented land use data directly in the land cadastre database, which requires simultaneous land use and cadastre maintenance. Based on the results for selected cadastral municipalities, we critically evaluated the proposed method.

Spike neural network as a controller in SDN network

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.