Deep learning technology promotes the development of neural network machine translation (NMT). End-to-End (E2E) has become the mainstream in NMT. It uses word vectors as the initial value of the input layer. The effect of word vector model directly affects the accuracy of E2E-NMT. Researchers have proposed many approaches to learn word representations and have achieved significant results. However

With the gradual development of bidirectional interacted future distribution network, it is necessary to enter distributed clean power sources with various characteristics, including wind turbine and solar panel. Traditional centralized control has difficulties to fulfill the demands of future distribution networks for safe, stable, and efficient operation. Aiming at the constrained power allocation

In the previous works, a discrete-time microstructure (DTMS) model for financial market was constructed by using identification technology and was successfully applied to dynamic asset allocation based on the identified excess demand. However, the initial value setting of the parameters has a great influence on the estimated results of the DTMS model, which may make the estimated model to describe

The large amount of network traffic generated by Internet applications brings great challenges to Internet security. In order to facilitate network management and realize automatic classification of network traffic, this paper proposes a network traffic classification model NTCNET based on CNNs. Use open data set to do simulation verification experiment, then compare the test results with a variety

In this paper, an automatic RGBD object segmentation method is described. The method integrates depth feature with the cues from RGB images and then uses maximal similarity based region merging (MSRM) method to obtain the segmentation results. Firstly, the depth information is fused to the simple linear iterative clustering (SLIC) method so as to produce superpixels whose boundaries are well adhered

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters

With the continuous development of computer vision technology and the continuous upgrading of digital imaging equipment, image depth measurement method is widely used in the fields of intelligent robotics, traffic assistance, three-dimensional modeling and three dimensional video production. The following are the drawbacks of the traditional depth information measurement method: the operation is complex

To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised

The 3D measurement system based on line-structured light uses a camera to capture laser stripes due to changing in the shape of an object, and uses the acquired pixel coordinates for 3D reconstruction. System calibration is an important step in 3D measurement. The current camera calibration algorithm research mainly focuses on improving the algorithm itself, and there is less research on the influence

In order to solve the technical problem of three-dimensional profile measurement of aero-engine blades with high speed rotation, an optical dynamic measurement system for aero-engine blades was developed. Firstly, the system is calibrated by the algorithm of spatial truncation phase calibration to establish the index between truncation phase and spatial coordinates. During the measurement, the deformation

With the continuous development of the robot industry, both industrial robots and collaborative robots are developing towards light type and intelligence. The core issue is that how to improve the dynamic control performance of robots and reduce costs. The accurate torque feedback control can be achieved by introducing a joint torque sensor. The disadvantages brought by it are higher cost and the limited

Considering the functional attributes of public bicycle outlets, users’ travel destinations and travel distances, this paper proposes a key node optimization scheme for urban public bicycle networks based on the combination of key nodes and wavefront theory. First analyze the net wave surface flow during peak hours to determine key nodes, then schedule or add nodes to achieve normal diversion in the

In the islanding micro-grid operation mode, due to the lack of support from the large power grid, the voltage of the bus and each node in the network is completely supported by the cooperation of the micro-grid inverters in the grid. Therefore, the control performance of the micro-grid inverter determines the quality of the power supply voltage. This paper proposes an improved AC and DC islanding micro-grid

Accurate analysis of targeted sentiment in online course reviews helps in understanding emotional changes of learners and improving the course quality. In this paper, we propose a fine-tuned bidirectional encoder representation from transformers (BERT) model for targeted sentiment analysis of course reviews. Specifically, it consists of two parts: binding corporate rules — conditional random field

The distribution energy router (DER) is the core of the interacted and interconnected micro grid in future distribution network, which fully meets the needs of the ubiquitous power internet of things (IOT) based future distribution network. The reliability of micro grid which applies the DER is highly related to its cascaded full-bridge converters. With the redundant full-bridge converters and IOT

With the continuous development of society, the social division of labor is further improved, and social production tends to be highly specialized and industrialized. Moreover, enterprise production is increasingly internationalized, and sales are gradually expanding. Therefore, the multi-objective sequencing in logistics distribution is incorporated into the path optimization of the logistics system

Neural Machine Translation (NMT) model has become the mainstream technology in machine translation. The supervised neural machine translation model trains with abundant of sentence-level parallel corpora. But for low-resources language or dialect with no such corpus available, it is difficult to achieve good performance. Researchers began to focus on unsupervised neural machine translation (UNMT) that

In the field of intelligent transportation, background complexity, lighting changes, occlusion, and scale transformation affect the tracking results of moving vehicles in the video. We propose an improved vehicle object tracking algorithm based on Multi-Domain Convolutional Neural Networks (MDNet), combining the instance segmentation method with the MDNet algorithm, adding two attention mechanisms

The transformation of taxation processes and the optimization and modeling of their management systems are hot topics in many disciplines such as public management and computer science. Therefore, the intelligent tax management is used to implement the tax process. Meanwhile, tax indicators are adopted as independent variables, and the Logistic regression model is applied to check the selected cases

In the information extraction, information sources can be screened according to the characteristics of the target network at the present stage, and the knowledge graph generated thereby can play a role in assisting the security analysis of the general network or power grid control network, mobile Internet and other special networks. In the method proposed in this paper, knowledge reasoning is mainly

Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This

A Technical Document (TD) is mainly composed by a set of modalities appropriately structured and associated. These modalities could be NL-text, block diagrams, formulas, tables, graphics, pictures etc. A deep understanding of a TD will be based on the synergistic understanding and associations of these modalities. This paper offers a novel methodology for the implementation of a holistic approach for

This paper presents a new hybrid concession mechanism for negotiating agents. It considers both the current concession behavior of the proposing agent and the concession offered by its opponent in the last counteroffer to create a new offer. The proposed mechanism is a kind of imitating offer generation tactic. The difference is that it uses the first order difference between the two last counteroffers

Multi-Agent Systems are susceptible to external disturbances, sensor failures or collapse of communication channel/media. Such failures disconnect the agent network and thereby hamper the consensus of the system. Quick recovery of consensus is vital to continue the normal operation of an agent-based system. However, only limited works in the past have investigated the problem of recovering the consensus

In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF)

A question answering system is a type of information retrieval that takes a question from a user in natural language as the input and returns the best answer to it as the output. In this paper, a medical question answering system in the Persian language is designed and implemented. During this research, a dataset of diseases and drugs is collected and structured. The proposed system includes three

In some circumstances, the performance of machine learning (ML) tasks are based on the quality of signal (data) that is processed in these tasks. Therefore, the pre-processing techniques, such as reconstruction and denoising methods, are important techniques in ML tasks. In reconstructed (estimated) method, the fused lasso algorithm with non-convex penalty function is an efficient method when the signal

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding

The pieces of information that are being collected from regions of disaster is critical as the rapid deployment of the first responders rely on them. Another critical part of that deployment is the acquisition of different types of information (visual, sounds, and others). Even with that information the rescuing teams still face the difficult task of rescuing humans under debris. Some of the constraints

As the dairy products have a short consumption period, the accurate prediction of their demand is very important for the dairy industry. Accordingly, this research specifically addresses the prediction of dairy product demand (DPD). The main contribution of this research is to provide an integrated framework based on statistical tests, time-series prediction and artificial intelligence with the runner-root

A systematic approach is developed for constructing proper trial solutions to Partial Differential Equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions. The spatial domain considered is of the rectangular hyper-box type. On each face either Dirichlet or Neumann conditions may apply. Robin conditions may be accommodated as well

Stochastic Gradient Descent (SGD) is perhaps the most frequently used method for large scale training. A common example is training a neural network over a large data set, which amounts to minimizing the corresponding mean squared error (MSE). Since the convergence of SGD is rather slow, acceleration techniques based on the notion of “Mini-Batches” have been developed. All of them however, mimicking

Within this paper, we present two neural nets for view-independent complex human activity recognition (HAR) from video frames. For our study here, we reduce the number of frames produced by a video sequence given that we can identify activities from a sparsely sampled sequence of body poses, and, at the same time, we are able to reduce the processing complexity and response while hardly affecting the

Traffic Sign Recognition (TSR) is a crucial component in many automotive applications, such as driver assistance, sign maintenance, and vehicle autonomy. In this paper, we present an efficient approach to training a machine learning-based TSR solution. In our choice of recognition method, we have opted for convolutional neural networks, which have demonstrated best-in-class performance in previous

Sentiment analysis is a circumstantial analysis of text, identifying the social sentiment to better understand the source material. The article addresses sentiment analysis of an English-Hindi and English-Bengali code-mixed textual corpus collected from social media. Code-mixing is an amalgamation of multiple languages, which previously mainly was associated with spoken language. However, social media

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror

We study the problem of finding the global optimum of a nonlinear real function over an interval box by means of complete search techniques, namely interval branch-and-bound algorithms. Such an algorithm typically generates a tree of boxes from the initial box by alternating branching steps and contraction steps in order to remove non optimal sub-boxes. In this paper, we introduce a new contraction

Deep neural networks become more popular as its ability to solve very complex pattern recognition problems. However, deep neural networks often need massive computational and memory resources, which is main reason resulting them to be difficult efficiently and entirely running on embedded platforms. This work addresses this problem by saving the computational and memory requirements of deep neural

We propose a logical encoding of argumentation frameworks with higher-order interactions (i.e. attacks/supports whose targets are arguments or other attacks/supports) with an evidential meaning for supports. Our purpose is to separate the logical expression of the meaning of an attack or an evidential support (simple or higher-order) from the logical expression of acceptability semantics. We consider

Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures

Possibilistic graphical models are powerful modeling and reasoning tools for uncertain information based on possibility theory. In this paper, we provide an analysis of computational complexity of MAP and MPE queries for possibilistic networks. MAP queries stand for maximum a posteriori explanation while MPE ones stand for most plausible explanation. We show that the decision problems of answering

Constraint Programming (CP) is a powerful declarative programming paradigm where inference and search are interleaved to find feasible and optimal solutions to various type of constraint systems. However, handling logical connectors with constructive information in CP is notoriously difficult. This paper presents If Then Else (ITE), a lightweight implementation of stratified constructive reasoning

This papera describes a maintenance scheduling application, which was developed together with an industrial partner. This is a highly combinatorial decision process, to plan and schedule the work of a group of travelling repair technicians, which perform preventive and corrective maintenance tasks at customer locations. Customers are located both in urban areas, where many customers are in close proximity

Big Data analytics is presently one of the most emerging areas of research for both organizations and enterprises. The requirement for deployment of efficient machine learning algorithms over huge amounts of data led to the development of parallelization frameworks and of specialized libraries (like Mahout and MLlib) which implement the most important among these algorithms. Moreover, the recent advances

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction

In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication. In this way, it essentially creates a parallel ensemble of optimization methods. At the same time, the system contains a planner that decides which of the available optimization methods

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human

We introduce an enhanced notion of unsatisfiable cores for QBF in prenex CNF that allows to weaken universal quantifiers to existential quantifiers in addition to the traditional removal of clauses. The resulting unsatisfiable cores can be different from those of the traditional notion in terms of syntax, standard semantics, and proof-based semantics. This not only gives rise to explanations of unsatisfiability

Learners’ opinions constitute an important source of information that can be useful to teachers and educational instructors in order to improve learning procedures and training activities. By analyzing learners’ actions and extracting data related to their learning behavior, educators can specify proper learning approaches to stimulate learners’ interest and contribute to constructive monitoring of

We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession

Does a post with specific emotional content that is posted on Twitter by an influential user have the capability to affect and even alter the opinions of those who read it? Accordingly, “influential” users affected by this post can then affect their followers so that eventually a large number of users may change their opinions about the subject the aforementioned post was made on? Social Influence

Microblog posts such as tweets frequently contain users’ opinions and thoughts about events, products, people, institutions, etc. However, the usage of social media to prop-agate hate speech is not an uncommon occurrence. Analyzing hateful speech in social media is essential for understanding, fighting and discouraging such actions. We believe that by extracting fragments of text that are semantically

Drug discovery is a complicated, time-consuming and expensive process. The cost for each new molecular entity (NME) is estimated at $1.8 billion. Furthermore, for a new drug to be FDA approved it often takes nearly a decade and approximately 20 new drugs being approved by the US Food and Drug Administration (FDA) each year. Accurately predicting drug-target interactions (DTIs) by computational methods

The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different

The rapid evolution of social networks in recent years has focused the attention of researchers to find adequate solutions for the management of these networks. For this purpose, several efficient algorithms dedicated to the tracking and the rapid detection of the community structure have been proposed. In this paper, we propose a novel density-based approach with dual optimization for tracking community

Taking videos as nodes in a graph, graph matching is an effective technique for unsupervised video-based person re-identification (re-ID). However, most of existing methods are sensitive to noisy training data and mainly only focus on visual content relations between query and gallery videos, which may introduce large amount of false positives. To enhance the robustness to training data and alleviate

Recommender systems’ evaluation is usually based on predictive accuracy and information retrieval metrics, with better scores meaning recommendations are of higher quality. However, new algorithms are constantly developed and the comparison of results of algorithms within an evaluation framework is difficult since different settings are used in the design and implementation of experiments. In this

A considerable part of social network analysis literature is dedicated to determining which individuals are to be considered as influential in particular social settings. Concretely, Social Influence can be described as the power or even the ability of a person to yet influence the thoughts as well as the actions of other users. So, User Influence stands as a value that depends on the interest of the

A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based

Location-Routing Problem (LRP) is a challenging problem in logistics, which combines two types of decision: facility location and vehicle routing. In this paper, we focus on LRP with multiple capacitated depots and one uncapacitated vehicle per depot, which has practical applications such as mail delivery and waste collection. We propose a simple iterated variable neighborhood search with an effective