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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.

We studied the dynamics of a neural network that has both recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity was sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns. We then applied this model to represent the trace eyeblink conditioning, which is mediated by the hippocampus. We assumed this model as CA3 of the hippocampus and considered an output neuron corresponding to a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during trace eyeblink conditioning, which was experimentally observed.

Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

This paper introduces a new framework for the design of parallel algorithms that may be executed on multiprogrammed architectures with variable resources. These features, in combination with an implied ability to handle fault tolerance, facilitates environments such as the GRID. A new model, BSPGRID is presented, which exploits the bulk synchronous paradigm to allow existing algorithms to be easily adapted and used. It models computation, communication, external memory accesses (I/O) and synchronization. By combining the communication and I/O operations BSPGRID allows the easy design of portable algorithms while permitting them to execute on non-dedicated hardware and/or changing resources, which is typical for machines in a GRID. However, even with this degree of dynamicity, the model still offers a simple and tractable cost model. Each program runs in its own virtual BSPGRID machine. Its emulation on a real computer is demonstrated to show the practicality of the framework. A dense matrix multiplication algorithm and its emulation in a multiprogrammed environment is given as an example.

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.

This paper introduces a discrete time model for time-variant delays and investigates the very nature of such delays. It is shown that a linear system-delay interface is a system theoretic necessity for the construction of composite linear systems with time-variant delays. Based on this analysis, two interfaces of particular importance are presented and used to obtain new, simple to check stability results for queue control systems. The relevance of the presented modeling and stability results on queue control systems to QoS control in modern communication networks is illustrated via several examples.

The main function of the on-load tap changing (OLTC) regulators consists of maintaining a constant voltage in order to feed critical loads despite the load changes or voltage changes in the ac mains. The traditional regulators are still used nowadays, but they present several disadvantages, like a slow response, which reaches from 100 ms to several seconds. These drawbacks can be overcome if the OLTC regulators would have shorter response time, commuting several times every cycle of the mains. There are two basic topologies for fast OLTC regulators. The first one consists of several taps and uses hard switching. The second one consists of two main switches commuting at high frequency, using soft-switching in order to reduce the power losses. The present topology is of the second type. This paper presents a mathematical model of the power stage of the proposed regulator. The model includes the parasitic resistances and the leakage inductances in order to obtain a better comprehension of the regulator operation. A parametric analysis has been done in order to observe the influence of the parasitic elements in the performance of the main parameters of the topology. The model is verified by experimental results obtained using a 500-W prototype.

In this paper, a software toolchain is presented for the fully automatic alignment of a 3D human face model. Beginning from a point cloud of a human head (previously segmented from its background), pose normalization is obtained using an innovative and purely geometrical approach. In order to solve the six degrees of freedom raised by this problem, we first exploit the human face's natural mirror symmetry; secondly, we analyze the frontal profile shape; and finally, we align the model's bounding box according to the position of the tip of the nose. The whole procedure is considered as a two-fold, multivariable optimization problem which is addressed by the use of multi-level, genetic algorithms and a greedy search stage, with the latter being compared against standard PCA. Experiments were conducted utilizing a GavabDB database and took into account proper preprocessing stages for noise filtering and head model reconstruction. Outcome results reveal strong validity in this approach, however, at the price of high computational complexity.

The noise of the current accumulator is analyzed. A model of time-delay-integration (TDI) CMOS image sensor is presented, which is used to analyze the noise performance. In this model, input signals are accumulated four times by the type of current and then converted to digital signals to accomplish the other accumulation by 32 times, i.e., 4 × 32 accumulation mode. The noise, which includes switch charge injection, sample noise and kT/C noise, is considered in this model. The major source of the noise and the relationship between noise and sample capacitance are evaluated through the model simulation. The results indicate that the total noise can be restrained by increasing sample capacitance. When the input signal is arranging from 0 μA to 100 μA, the accuracy of the current accumulator can be 11 bits by using 1 pF sample capacitor. The SNR of the output signal can be increased by 20.38 dB which is close to the ideal result. The circuit of the current accumulator based on the model is also proposed.

It is well known that every sequential element may become metastable when provided with marginal inputs, such as input transitions occurring too close or input voltage not reaching a defined HI or LO level. In this case the sequential element requires extra time to decide which digital output level to finally present, which is perceived as an output delay. The amount of this delay depends on how close the element’s state is to the balance point, at which the delay may, theoretically, become infinite. While metastability can be safely avoided within a closed timing domain, it cannot be completely ruled out at timing domain boundaries. Therefore it is important to quantify its effect. Traditionally this is done by means of a “mean time between upsets” (MTBU) which gives the expected interval between two metastable upsets. The latter is defined as the event of latching the still undecided output of one sequential element by a subsequent one. However, such a definition only makes sense in a time-safe environment like a synchronous design. In this paper we will extend the scope to so-called value-safe environments, in which a sequential element can safely finalize its decision, since the subsequent one waits for completion before capturing its output. Here metastability is not a matter of “failure” but a performance issue, and hence characterization by MTBU is not intuitive. Therefore we will put the focus on the delay aspect and derive a suitable model. This model extends existing approaches by also including the area of very weak metastability and thus providing complete coverage. We will show its validity through comparison with transistor-level simulation results for the most popular sequential elements in different implementations, point out its relation to the traditional MTBU model parameters, namely $\tau$ and $T_0$, and show how to use it for calculating the performance penalty in a value-safe environment.

In this work, a surface potential modeling approach has been proposed to model dual gate, bilayer graphene field effect transistor. The equivalent capacitive network of GFET has been improved considering the quantum capacitance effect for each layer and interlayer capacitances. Surface potentials of both layers are determined analytically from equivalent capacitive network. The explicit expression of drain to source current is established from drift-diffusion transport mechanism using the surface potentials of the layers. The drain current characteristics and transfer characteristics of the developed model shows good agreement with the experimental results in literatures. The small signal parameters of intrinsic graphene transistor i.e., output conductance ($g_{\mathrm{\text{ds}}}$), transconductance ($g_m$), gate to drain capacitance ($C_{\mathrm{\text{gd}}}$) and gate to source capacitance ($C_{\mathrm{\text{gs}}}$) have been derived and finally, the cut-off frequency is determined for the developed model. The model is compared with reported experimental data using Normalized Root Mean Square Error (NRMSE) metric and it shows less than $16\%$ NRMSE. A Verilog-A code has been developed for this model and a single ended frequency doubler has been designed in Cadence Design environment using this Verilog-A model.

In current literature few detailed process models for Requirements Engineering are presented: usually high-level activities are distinguished, without a more precise specification of each activity. In this paper the process of Requirements Engineering has been analyzed using knowledge-level modelling techniques, resulting in a well-specified compositional process model for the Requirements Engineering task. This process model is considered to be a generic process model: it can be refined (by instantiation or specialisation) into a process model for a specific kind of Requirements Engineering process.

Mandatory access control (MAC) mechanisms control which users or processes have access to which resources in a system. MAC policies are increasingly specified to facilitate managing and maintaining access control. However, the correct specification of the policies is a very challenging problem. To formally and precisely capture the security properties that MAC should adhere to, MAC models are usually written to bridge the rather wide gap in abstraction between policies and mechanisms. In this paper, we propose a general approach for property verification for MAC models. The approach defines a standardized structure for MAC models, providing for both property verification and automated generation of test cases. The approach expresses MAC models in the specification language of a model checker and expresses generic access control properties in the property language. Then the approach uses the model checker to verify the integrity, coverage, and confinement of these properties for the MAC models and finally generates test cases via combinatorial covering array for the system implementations of the models.

The architecture of a software system results from decisions made by the developers throughout the software life cycle. Any decision pertaining to software architecture is called an architectural decision. Architectural decision modelling captures the dependencies that exist between the decisions and serves as a foundation for knowledge management and reuse. Several models have been described in the literature, using natural language to explain the basic notions and class diagrams to show relations between them. However, a formal definition of an architectural decision is still missing. This paper analyzes existing architectural decision models and provides a formal background for the basic notions that all the models have consensus on. The major contribution of this paper is twofold: to propose a set-theoretic definition of the semantics of architectural decisions; and to show an explicit interpretation of basic relationships that exist in the architectural knowledge. The formalization can help in understanding the meaning of architectural decisions and the meaning of relations that exist between the decision elements. UML-based metamodel for architectural design decisions is also presented.

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 order to explore the application of IoT technology in robots and the promotion of IoT robot technology to the economy, by comparing traditional technology and IoT intelligent robot technology, this article combines it with economic development to analyze the promotion of IoT robot to economic development. Based on the ultra-wideband ranging method, this paper designs an ultra-wideband radio frequency positioning system and applies it to the robot’s positioning process. Moreover, this article combines the application of robots in the current social and economic development to construct the system structure, and conducts functional analysis with manufacturing robots and monitoring robots as the main body. After constructing an intelligent robot based on the Internet of Things technology, by comparing the traditional technology and the intelligent robot technology of the Internet of Things, this article combines it with economic development to analyze the promotion of IoT robot to economic development. From the analysis results of this article, it can be seen that the advancement of IoT robot technology can effectively promote economic development.

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.