Narrow Results

Data lakes are storage repositories that contain large amounts of data (big data) in its native format; encompassing structured, semi-structured or unstructured. Data lakes are open to a wide range of use cases, such as carrying out advanced analytics and extracting knowledge patterns. However, the sheer dumping of data into a data lake would only lead to a data swamp. To prevent such a situation, enterprises can adopt best practices, among which to manage data lake metadata. A growing body of research has focused on proposing metadata systems and models for data lakes with a special interest on model genericness. However, existing models fail to cover all aspects of a data lake, due to their static modeling approach. Besides, they do not fully cover essential features for an effective metadata management, namely governance, visibility and uniform treatment of data lake concepts. In this paper, we propose a dynamic modeling approach to meet these features, based on two main constructs: data lake concept and data lake relationship. We showcase our approach by Megale, a graph-based metadata system for NoSQL data lake exploration. We present a proof-of-concept implementation of Megale and we show its effectiveness and efficiency in exploring the data lake.

Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best.

Traditional GPS positioning technology cannot be used in indoor space. With the development of the new positioning technology and the Internet of things, the indoor mobile object positioning and navigation model have been the focus of the relevant research institutions at home and abroad. Based on this, indoor positioning technology was studied starting from Wi-Fi, RFID, and iBeacon technology in this paper. However, the accuracy of indoor positioning and navigation needs to be further improved. This paper presents a semantic space model based on artificial intelligence technology, through semantic pattern matching, semantic concept extension, semantic reasoning and semantic mapping, and interior semantic localization is realized. The indoor semantic network and indoor grid navigation model are constructed, and the indoor semantic path is modeled from time, location, user, and congestion. At the same time, the improved Term Frequency-Inverse Document Frequency is combined with the Hidden Markov Model to improve the accuracy of matching the stay area with the most likely location to visit and improve the accuracy of semantic annotation. It was found that the research on the indoor positioning and navigation model based on the semantic grid can realize the uniform expression of the complex spatial semantics of the theme, geometry, connectivity, and distance, which can promote the development of indoor positioning and navigation.

We propose a method for the 3D segmentation and representation of cortical folds with a special emphasis on the cortical sulci. These cortical structures are represented using "active ribbons". Active ribbons are built from active surfaces, which represent the median surface of a particular sulcus filled by CSF. Sulci modeling is obtained from MRI acquisitions (usually T1 images). The segmentation is performed using an automatic labeling procedure to separate gyri from sulci based on curvature analysis of the different iso-intensity surfaces of the original MRI volume. The outer parts of the sulci are used to initialize the convergence of the active ribbon from the outer parts of the brain to the interior. This procedure has two advantages: first, it permits the labeling of voxels belonging to sulci on the external part of the brain as well as on the inside (which is often the hardest point) and secondly, this segmentation allows 3D visualization of the sulci in the MRI volumetric environment as well as showing the sophisticated shapes of the cortical structures by means of isolated surfaces. Active ribbons can be used to study the complicated shape of the cortical anatomy, to model the variability of these structures in shape and position, to assist nonlinear registrations of human brains by locally controlling the warping procedure, to map brain neurophysiological functions into morphology or even to select the trajectory of an intra-sulci (virtual) endoscope.

To overcome the limitations of mobile devices in executing computing-intensive workloads, mobile edge computing emerged at the times required. It can effectively support computing intensive and delay critical applications executed by Internet of Things devices with limited computing power and energy constraints and becomes the key technology of next-generation networks. This research first determined the optimization framework of the mobile edge computing task unloading system, built a basic platform for mobile edge computing task unloading after system optimization, completed the drill transformation of intelligent algorithm based on the mobile edge computing task unloading algorithm of the optimized system, and studied its convergence and applicability. Finally, by comparing several different mobile edge computing tasks unloading models, Select a suitable mobile edge computing task unloading model, and complete the practical effect test. The results show that: (1) Compared with the traditional unloading mode, the optimized mobile edge computing task unloading system has higher work efficiency; (2) The whole process model of mobile edge computing task unloading completed in this study can reduce the task processing delay in the actual use process; (3) The unloading system with intelligent algorithms is more suitable for edge devices, providing a reference task unloading model for engineering practice.

In this paper, we study the uncertain multiple attribute decision making problems with preference information on alternatives (UMADM-PIA, for short), in which the information on attribute weights is not precisely known, but value ranges can be obtained. A projection method is proposed for the UMADM-PIA. To reflect the decision maker's preference information, a projection model is established to determine the weights of attributes, and then to select the most desirable alternative(s). The method can reflect both the objective information and the decision maker's subjective preferences, and can also be performed on computer easily. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and practicality.

Construction processes planning and effective management are extremely important for success in construction business. Head of a design must be well experienced in initiating, planning, and executing of construction projects. Therefore, proper assessment of design projects' managers is a vital part of construction process. The paper deals with an effective methodology that might serve as a decision support aid in assessing project managers. Project managers' different characteristics are considered to be more or less important for the effective management of the project. Qualifying of managers is based on laws in force and sustainability of project management involving determination of attributes value and weights by applying analytic hierarchy process (AHP) and expert judgement methods. For managers' assessment and decision supporting is used additive ratio assessment method (ARAS). The model, presented in this study, shows that the three different methods combined (ARAS method aggregated together with the AHP method and the expert judgement method) is an effective tool for multiple criteria decision aiding. As a tool for the assessment of the developed model, was developed multiple criteria decision support system (MCDSS) weighting and assessment of ratios (WEAR) software. The solution results show that the created model, selected methods and MCDSS WEAR can be applied in practice as an effective decision aid.

A new model for the control of financial processes based on metric graphs is presented. Our motivation has its roots in the current interest in finding effective algorithms to detect and classify relations among elements of a social network. For example, the analysis of a set of companies working for a given public administration or other figures in which automatic fraud detection systems are needed. Given a set $\mathrm{\Omega }$ and a proximity function $\varphi :\mathrm{\Omega }\times \mathrm{\Omega }\to {ℝ}^{+}$, we define a new metric for $\mathrm{\Omega }$ by considering a path distance in $\mathrm{\Omega }$ that is considered as a graph. We analyze the properties of such a distance, and several procedures for defining the initial proximity matrix ${(\varphi (a,b))}_{(a,b)\in \mathrm{\Omega }\times \mathrm{\Omega }}$. Using this formalism, we state our main idea regarding fraud detection: financial fraud can be detected because it produces a meaningful local change of density in the metric space defined in this way.

In this paper, we describe a new type of humanoid robot designed for made-to-measure garment industry — a shape-changing robotic mannequin. This mannequin is designed to imitate body shapes of different people. The main emphasis of this paper is on modeling and shape-optimization algorithm used to adjust mannequins shape to resemble the shape of any given person. We represent the whole procedure of adjusting the mannequin to the body shapes of real people. Finally, we provide the estimate of the mannequin's model precision and suitability of the proposed solutions for made-to-measure tailoring application. The results show that the mannequin and the optimization methods are sufficiently precise for the requirements in tailoring industry.

Previous studies reported that children with autism spectrum disorder (ASD) show a certain interest in social robots. This makes social robots potential to be a model to teach social skills. This exploratory study aims to investigate whether three types of joint attention skills (i.e., eye-contact, pointing, gaze-following) could be improved for five preschoolers with ASD using an evidence-based robot-modeling intervention with a humanoid social robot NAO. Our observation shows that these children were motivated when interacting with NAO by following and responding correctly to NAO’s joint attention behaviors. Although some improvements were found, no pattern or systematic effect could be revealed. In the future, more evidence-based studies are needed to investigate the benefits of robot-assisted therapy more deeply.

Traffic accidents have become a more and more important factor to restrict the development of economy and affect the safety of human. Gray System quests for the inner relation through the original data, this is an approach to find out the rule of data through other data. Highway traffic accident forecasting model based on Gray System uses some original data, through theory of Gray System, processing the data and modeling GM (1,1). Through the validation of actual data, error of GM (1,1) is minor, it can be used in actual forecasting.

With respect to the common used recognition methods for driving intention, there is a lack of effective means to apply the recognition results to calculate shift schedule which can reflect the driving style. This paper demonstrates a recognition method of driving intention which can be used to optimize the shift schedule and can also reflect the driving style of individual driver. Combined with the operation experience from excellent drivers, the relationship model between the engine speed, the throttle opening, the change rate of throttle opening and the driver’s shifting performance expectations has been established and quantified using fuzzy logic. The simulation results show that the recognition results of the model are consistent with the driving habits of experienced drivers. The method overcomes the limitation of the current shift schedule of the vehicles with automatic transmission which mainly considers a single best performance, and meanwhile makes the gear schedule be more personalized.

The construction of generation scheduling optimization model has important theoretical and practical value for rich and development of power system optimization scheduling theory, and for promotion of power system safety and energy conservation. In the background of China's energy shortage, using interruptible load in power generation scheduling optimization can ease the power supply tension, improve the system security, and optimize the operation of power grid, so it lays the foundation for the promotion of the system reform of electric power. This paper constructs generation scheduling optimization model considering interruptible load and uses GAMS software to solve the proposed optimization model under the participation of interruptible load. The result shows that interruptible load can effectively reduce the load curve in the peak period, which provides spare larger space for the random grid.