Evaluation of the Project Management Competences Based on the Semantic Networks

The paper presents the testing and evaluation facilities of the SinPers system. The SinPers is a web based learning environment in project management, capable of building and conducting a complete and personalized training cycle, from the definition of the learning objectives to the assessment of the learning results for each learner. The testing and evaluation facilities of SinPers system are based on the ontological approach. The educational ontology is mapped on a semantic network. Further, the semantic network is projected into a concept space graph. The semantic computability of the concept space graph is used to design the tests. The paper focuses on the applicability of the system in the certification, for the knowledge assessment, related to each element of competence. The semantic computability is used for differentiating between different certification levels.testing, assessment, ontology, semantic networks, certification.

GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment

Effective biomedical literature retrieval (BLR) plays a central role in precision medicine informatics. In this paper, we propose GRAPHENE, which is a deep learning based framework for precise BLR. GRAPHENE consists of three main different modules 1) graph-augmented document representation learning; 2) query expansion and representation learning and 3) learning to rank biomedical articles. The graph-augmented document representation learning module constructs a document-concept graph containing biomedical concept nodes and document nodes so that global biomedical related concept from external knowledge source can be captured, which is further connected to a BiLSTM so both local and global topics can be explored. Query expansion and representation learning module expands the query with abbreviations and different names, and then builds a CNN-based model to convolve the expanded query and obtain a vector representation for each query. Learning to rank minimizes a ranking loss between biomedical articles with the query to learn the retrieval function. Experimental results on applying our system to TREC Precision Medicine track data are provided to demonstrate its effectiveness.Comment: CIKM 201

PENERAPAN KONSEP PEWARNAAN GRAF DALAM PENJADWALAN PEMBELAJARAN DI SMAN 1 KOPANG

Scheduling is a way to determine the time and place an activity will be carried out. A learning schedule that is free from overlapping scheduling problems needs to be available before teaching and learning activities begin so that the early teaching and learning activities can take place effectively. One way that can be used to overcome the problem of overlapping learning scheduling is to use the concept of graph coloring contained in the topic of graph theory. Therefore, the goal to be achieved in this study is to obtain a schedule of teaching and learning activities that are free from overlapping scheduling at SMAN 1 Kopang by applying the concept of graph coloring. The type of research used is applied research. Based on the scheduling data, we get a neighboring matrix with a size of 224×224 and a chromatic number of 22. The determination of neighboring matrices using the help of the Excel VBA programming language. The schedule-making begins by creating a scheduling conflict graph based on the lesson schedule data, then the graph obtained will be colored using Welch Powell's algorithm. After the coloring results are obtained, a learning schedule can be arranged based on the coloring results. Subjects of the same color can be scheduled at the same time and vice versa. The lesson schedule produced in this study requires six additional time slots so that the lesson schedule is free from scheduling overlap because the chromatic number obtained in graph coloring is greater than the available time slots at SMAN 1 Kopang

PENERAPAN KONSEP PEWARNAAN GRAF DALAM PENJADWALAN PEMBELAJARAN DI SMAN 1 KOPANG

Scheduling is a way to determine the time and place an activity will be carried out. A learning schedule that is free from overlapping scheduling problems needs to be available before teaching and learning activities begin so that the early teaching and learning activities can take place effectively. One way that can be used to overcome the problem of overlapping learning scheduling is to use the concept of graph coloring contained in the topic of graph theory. Therefore, the goal to be achieved in this study is to obtain a schedule of teaching and learning activities that are free from overlapping scheduling at SMAN 1 Kopang by applying the concept of graph coloring. The type of research used is applied research. Based on the scheduling data, we get a neighboring matrix with a size of 224×224 and a chromatic number of 22. The determination of neighboring matrices using the help of the Excel VBA programming language. The schedule-making begins by creating a scheduling conflict graph based on the lesson schedule data, then the graph obtained will be colored using Welch Powell's algorithm. After the coloring results are obtained, a learning schedule can be arranged based on the coloring results. Subjects of the same color can be scheduled at the same time and vice versa. The lesson schedule produced in this study requires six additional time slots so that the lesson schedule is free from scheduling overlap because the chromatic number obtained in graph coloring is greater than the available time slots at SMAN 1 Kopang

Fast Graph-Based Object Segmentation for RGB-D Images

Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods