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In this paper, we consider the issue of digital camera identification based on images. This topic matches the area of digital forensics. The problem is well known in the literature and many algorithms based on camera's fingerprint have been proposed. In this paper, we discuss the digital camera identification based on convolutional neural networks (CNN). CNNs are state-of-the-art method in computer vision and are widely utilized in many applications. Our goal is to find out whether it is possible to speed up the process of learning the networks by the images. We conduct a set of representative experiments which show that replacing the ReLU with SELU activation function and adjusting the network's hyperparamethers (e.g. learning rate) may have a significant impact on reduction time of learning. We also consider using the dropout layer. The experiments are held on representative image dataset, consisting of many images coming from modern cameras and show effectiveness of our propositions. [ABSTRACT FROM AUTHOR]

Community detection is a highly active research area that aims to identify groups of vertices with similar properties or interests within complex real-world networks. Over the years, a large number of papers have been published, resulting in the development of various community detection algorithms that consider factors such as the type of networks, the nature of communities, and applications. Despite numerous relevant review and survey papers, the literature lacks a comprehensive analysis of the computational complexity of existing community detection algorithms. This review aims to address this gap by providing a more detailed analysis and evaluation of the computational complexity of hierarchical clustering algorithms for community detection, including two main categories: agglomerative and divisive algorithms. We also highlight the main theoretical concepts, emphasizing the benefits and drawbacks of each approach, both in theory and in practical applications. This review helps researchers and practitioners in this field better understand valuable information on the differences and unique features of community detection algorithms with hierarchical community structure. [ABSTRACT FROM AUTHOR]

The mathematical skeleton of a complex form has been essential for a variety of scientific fields and of great interest to many researchers for decades. It is based on several concepts such as the reconstruction of forms and image processing. This paper aims to develop a novel mathematical algorithm to approximate the skeleton of a non-polygonal shape and to compare it to the most used methods. The mathematical technique of skeletonization is used as a reference to validate and compare the proposed method to the most used ones. The crux of the proposed technique is to Cartesianize the shape (polygonize in 2D), then skeletonize it. Moreover, this novel method is grounded upon the construction of bisectors on the simplex of the corresponding Cartesianized shape. Python is used to implement the algorithm proposed and test it on multiple shapes. The comparison of the results generated by the proposed algorithm and the Python predefined function skeletonize() shows that the proposed method is more effective and could be adjusted through the rate of Cartesianization of the target shape. The major contributions of this novel technique include the mitigation of some issues of existing methods, simplification, and optimization of the processing performance mainly in terms of algorithm complexity. [ABSTRACT FROM AUTHOR]

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable. [ABSTRACT FROM AUTHOR]

For many years now, the mining industry has seen a boost in exploring and developing the systems for monitoring operational parameters of mining machines, in particular load-haul-dump machines. Therefore, further researches on algorithmics have also advanced dynamically regarding effective performance management as well as predictive maintenance. Nonetheless, the issue of road conditions is still being neglected. That issue has a substantial impact on both the overall operator's convenience, their performance, and machinery reliability, especially its construction node and tire damages. Moreover, such negligence pertains also to the maintenance of mine infrastructure, including the network of passages. The paper explains the use of the portable inertial measurement unit (IMU) in evaluating road conditions in the deep underground mine. The detailed descriptions of the road quality classification procedure and bump detection have been included. The paper outlines the basic method of tracking the motion trajectory of vehicles and suggests the method of visualization of the results of the road conditions evaluation. This paper covers the sample results collected by the measurements unit in the deep underground mine during six experiments. This paper is an extended version of a paper presented at the ACIIDs 2020 conference [P. Stefaniak, D. Gawelski, S. Anufriiev and P. Śliwiński, Road-quality classification and motion tracking with inertial sensors in the deep underground mine, Asian Conference on Intelligent Information and Database Systems, March 2020, Springer, Singapore, pp. 168–178]. [ABSTRACT FROM AUTHOR]

Distributed Virtual Reality (DVR) is a combination of network and virtual reality technology, it could facilitate to construct a uniformly shared Distributed Virtual Environment (DVE) by using network to connect geographically distributed multiplayers. This paper concentrates on the theoretical research and practical development about Multiplayer Virtual Intelligent System (MVIS), and the main contribution could be summarized as two points. (1) Based on the DVR technology, this paper presented some theoretical research on MVIS, including the classification of virtual entities, communication pattern of entities, and the behavioral consistency research. Furthermore, a Multiplayer Earliest Deadline First (MEDF) program was proposed in order to guarantee the consistency of entities. (2) A prototype algorithm experiment system, called Multiplayer Graph-algorithm Intelligent System (MGIS), was designed. MGIS not onlyefficiently solves many problems in traditional computer algorithm teaching, such as high-abstraction, difficulty to understand, and lack of interaction mechanism; but also extends the application of DVR to cultural tourism, because MGIS is developed on the 3D scene of Lushan Mountain, which is one of the notable tourist attractions in China, and was included in the UNESCO World Heritage list in 1996. What i's more, MGIS illustrates the ability of expression, applicability and generality of the theoretical research about MVIS. [ABSTRACT FROM AUTHOR]

Industry 4.0 though launched less than a decade ago, has revolutionized the way technologies are being used. It has found its application in almost every field of manufacturing, cybersecurity, health, banking, and other services. Industry 4.0 is heavily dependent on interconnectivity and data. Machine learning (ML) acts as a foundation for building industry 4.0 applications. In this paper, we have provided a broad view of how ML is necessary to accomplish the benefits of industry 4.0. The paper includes ML usage in companies and the limitations of ML, which need to be mitigated. There are also some instances of the failure of ML algorithms and their repercussions. Though industry 4.0 requires a lot more inputs and capital than normal processes, the long-run benefits outweigh the initial costs. ML is gaining popularity, and extensive research is happening to exploit its potential and develop full smart applications. [ABSTRACT FROM AUTHOR]

Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking. [ABSTRACT FROM AUTHOR]

In this paper a systematic review is conducted to investigate the structure, components, techniques, evaluation procedure, and comparison scope related to prior ABE-based studies. The undeniable role of accurate development e®ort estimation in the success of software project management has attracted the attention of researchers over the past few years. Among various algorithmic and non-algorithmic estimation methods, analogy based estimation (ABE) is a widely accepted method due to its simplicity and estimation capability. This paper investigates the improvement process of ABE method during 2000 to 2012. Six research questions are defined to be addressed through evaluation of prior ABE-based studies. The review domain includes 24 papers selected through a tough filtration process. The results show that improvement of ABE can be performed through adjustment, grey theory, attribute weighting and attribute selection techniques. Moreover, ABE configurations can significantly a®ect the results. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

The technology for path planning of independent mobile robots is mature, but multi-robot path planning for unified scheduling and allocation is much more complex than single-robot path planning. This requires consideration of collision problems between robots, general optimal path problems, etc. This paper proposes the use of the BSO algorithm for unified scheduling and allocation of multiple robots to improve the efficiency of task execution. The BSO algorithm is a new type of intelligent optimization algorithm that uses clustering ideas to search for local optimal solutions and obtains global optimal solutions by comparing local optimal solutions. It also uses mutation ideas to increase the diversity of the algorithm and avoid becoming trapped in local optimal solutions. Using the GA/SA algorithm and the proposed BSO algorithm for computer simulation comparison, we obtained the optimal path planning for the three robots under unified scheduling. The total distance of the optimal path obtained by the BSO algorithm was 27.36% and 25.31% shorter than those of the GA and SA algorithms, respectively. To further test the performance of the BSO algorithm, we conducted additional experiments on the unified scheduling of multiple robots. The experimental results show that the proposed BSO algorithm can significantly improve the efficiency. The multi-robot under unified scheduling performs point-to-point path planning without collisions, and they can traverse all task target points in the shortest path without repetition. This algorithm is suitable for multi-robot tasks in large-scale environments. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel fractional Caputo-type simultaneous method for finding all simple and multiple roots of polynomial equations. Without any additional polynomial and derivative evaluations using suitable correction, the order of convergence of the basic Aberth–Ehrlich simultaneous method has been increased from three to α + 3. In terms of accuracy, residual graph, computational efficiency and computation CPU time, the newly proposed families of simultaneous methods outperforms existing methods in numerical applications. [ABSTRACT FROM AUTHOR]

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

Face sketch recognition has great practical value in the criminal detection, security and other fields. Especially, it can help the police narrow down potential suspects in criminal detection effectively. Face sketch represents the original photos in a simple and recognizable form, so sketch and photo are images of two different modes. In order to identify the corresponding sketch face image in a lot of photo face images, this paper presents an improved sketch-photo transformation algorithm, and it uses the effective characteristics of the photo image more reasonably during transforming a photo image into sketch. In this way, it can reduce the difference between the sketch and photo image to improve the matching effect, and save the recognition time. Many experiments on CUHK Face Sketch database including 188 sketch-photos prove the effectiveness of the method in this paper. [ABSTRACT FROM AUTHOR]

Redundancy rules in knowledge bases will affect the reasoning process of knowledge bases. And they will take up a lot of unnecessary memory space. So the notions of redundancy rules are briefly introduced. Meanwhile they are classified into four types. This paper studies the redundancy rules based on propositional logic and presents the reduction algorithms of four kinds of redundancy rules. They are equivalent redundancy rules, implication redundancy rules and cycle redundancy rules in explicit redundancy rules and condition redundancy rules in implicit redundancy rules. The reduction in this paper optimizes the structure of rule-based knowledge bases. And it also improves the efficiency of time and space of the reasoning on knowledge bases. [ABSTRACT FROM AUTHOR]

The objective of this paper is to apply clipping techniques on natural images and to analyze the performance of various clipping algorithms in computer graphics. The clipping techniques used in this paper is Cohen–Sutherland line clipping, Liang–Barsky line clipping, Nicholl–Lee–Nicholl line clipping and Sutherland–Hodgman polygon clipping. The clipping algorithms are evaluated by using the three parameters: time complexity, space complexity and image accuracy. Previously, there is no performance evaluation on clipping algorithms done. Motivating by this factor, in this paper an evaluation of clipping algorithms is made. The novelty of this paper is to apply the clipping algorithms on natural images. It is justified that the above mentioned clipping algorithms outperform well on clipping the natural images. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm for smoke region of focus (ROF) detection based on surveillance video is proposed in order to solve the problem of limited application in scenes range and imaging environment of the traditional smoke detection algorithm. The frog vision perception model is used in this algorithm. First the suspect regions are detected, and then the static and the dynamic features of the regions are chosen for the smoke ROF detection. Experimental results show that the algorithm is efficient and significant for improving the operational rate of the detection. [ABSTRACT FROM AUTHOR]

In practical applications of graph theory, non-deterministic factors are frequently encountered. This paper employs uncertainty theory to deal with non-deterministic factors in problems of graph connectivity. The concepts of uncertain graph and connectedness index of uncertain graph are proposed in this paper. It presents two algorithms to calculate connectedness index of an uncertain graph. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users' actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics. [ABSTRACT FROM AUTHOR]

Due to more severe challenges of traffic safety problems, the Advanced Driver Assistance Systems (ADAS) has received widespread attention. Measuring the distance to a preceding vehicle is important for ADAS. However, the existing algorithm focuses more on straight road sections than on curve measurements. In this paper, we present a novel measuring algorithm for the distance to a preceding vehicle on a curve road using on-board monocular camera. Firstly, the characteristics of driving on the curve road is analyzed and the recognition of the preceding vehicle road area is proposed. Then, the vehicle detection and distance measuring algorithms are investigated. We have verified these algorithms on real road driving. The experimental results show that this method proposed in the paper can detect the preceding vehicle on curve roads and accurately calculate the longitudinal distance and horizontal distance to the preceding vehicle. [ABSTRACT FROM AUTHOR]

The paper presents a hybrid cloud service provisioning and selection optimization scheme, and proposes a hybrid cloud model which consists of hybrid cloud users, private cloud and public cloud. This scheme aims to effectively provide cloud service and allocate cloud resources, such that the system utility can be maximized subject to public cloud resource constraints and hybrid cloud users constraints. The paper makes use of a utility-driven approach to solve interaction among private cloud user, hybrid cloud service provider and public cloud provider in hybrid cloud environment. The paper presents hybrid cloud service provisioning and selection algorithm in hybrid cloud. The hybrid cloud market consists of hybrid cloud user agent, hybrid cloud service agent and hybrid cloud agent, which represent the interests of different roles. The experiments are designed to compare the performance of proposed algorithm with the other related work. [ABSTRACT FROM AUTHOR]

Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches. [ABSTRACT FROM AUTHOR]

In order to improve the H.264/AVC compressed video stream error resilience in wireless channel transmission, this paper presents a spatial error concealment algorithm based on adaptive edge threshold and directional weight. Firstly, this algorithm makes use of Sobel gradient operator of image edge detection to detect the edge of adjacent macro blocks; secondly, according to specific information of adjacent macro-block of damaged macro-block, it can set gradient adaptive threshold; thirdly, it makes the direction weighted interpolation to damaged macro-block with the Sobel gradient operator of image edge detection. Experiments show that the image reconstruction quality is greatly improved by using this algorithm, which has higher application value for different video sequence as compared to the traditional spatial error concealment algorithms. This algorithm not only improves the quality of image restoration, but also has higher application value. [ABSTRACT FROM AUTHOR]

The process of missile launch training was confined to the virtual scene in the past. So, cooperating with an artillery college, the group makes the moving target detection technology to be applied in missile training equipment, so as to make the training apply to the field operations. This paper presents the frame difference mapping algorithm, which is used to detect the moving target in the background of moving video frame. According to the target region which is given out by the system in the graphical interface, the students do the launching missile training. The moving target detection algorithm which is provided with the low complexity and the high accuracy, i.e. proposed by the paper, is based on Gauss mixture model and frame difference mapping. The mechanism of layered-graphics and the message agent which makes the modules in the system be independent of each other are used in the system designing. So, the module coupling degree in terms of this mechanism is lower than before. This mechanism brings convenience to system maintenance and upgrade, especially for the system's transplanting to the real missile launch system in future. [ABSTRACT FROM AUTHOR]

The aim of the paper is to use Arbitrary Lagrangian Eulerian (ALE) formulation for fluid-structure interaction for modeling blood flow in artery. Predicting blood flow and its effects on arteries requires simulation of fluid-structure coupling with deformable mesh. For fluid simulation velocity-pressure formulation is used, we present the algorithm which allows to compute fluid velocity and pressure using explicit time integration. This method has been applied successfully for several applications including sloshing tank analysis. For the structure shell type elements with five points integration through the thickness to accurately represent bending effects, are modeled. Since the structure is deformable, to prevent high mesh distortion an elasticity material model for the mesh is used for mesh deformation. For fluid-structure coupling, explicit contact algorithm based on penalty method. Such a model can be used to study the profile of the flow and pressure waves as they propagate along the arteries. In the paper, the onset of a pressure pulse was simulated at the entrance of a three dimension straight artery blood vessel and the resulting dynamic response in the form of a propagating pulse wave through the wall was analyzed and compared. Good agreement was found between the numerical results and the theoretical description of an idealized artery. Work has also been done on implementing the material constitutive models specific for vascular applications. [ABSTRACT FROM AUTHOR]

How to solve constrained optimization problems constitutes an important part of the research on optimization problems. In this paper, a hybrid immune clonal particle swarm optimization multi-objective algorithm is proposed to solve constrained optimization problems. In the proposed algorithm, the population is first initialized with the theory of good point set. Then, differential evolution is adopted to improve the local optimal solution of each particle, with immune clonal strategy incorporated to improve each particle. As a final step, sub-swarm is used to enhance the position and velocity of individual particle. The new algorithm has been tested on 24 standard test functions and three engineering optimization problems, whose results show that the new algorithm has good performance in both robustness and convergence. [ABSTRACT FROM AUTHOR]

Automating fabric defect detection has a significant role in fabric industries. However, the existing fabric defect detection algorithms lack the real-time performance that is required in real applications due to their high demanding computation. To ensure real time, high accuracy and reliable fabric defect detection this paper developed a fast and parallel normalized cross-correlation algorithm based on summed-area table technique called PFDD-SAT. To meet real-time requirements, extensive use of the NVIDIA CUDA framework for Graphical Processing Unit (GPU) computing is made. The detailed implementation steps of the PFDD-SAT are illustrated in this paper. Several experiments have been carried out to evaluate the detection time and accuracy and then the robustness to illumination and Gaussian noises. The results show that the PFDD-SAT has robustness to noise and speeds the defect detection process more than 200 times than normal required time and that greatly met the needs for real-time automatic fabric defect detection. [ABSTRACT FROM AUTHOR]

The object deployment of a distributed software has a great impact on its performance. In this paper an analytical model for performance evaluation of different object deployments, is presented. The key advantage of the proposed model over the traditional Queuing Network models is the usefulness in the deployment optimization when the search space is huge and automatic instantiation of Queuing performance models corresponding to an object deployment is costly. Since our model produces an optimal deployment corresponding to each input load separately, the runtime behavior of the software corresponding to each input load should be profiled using simulation first. In this paper a translation scheme for generating the simulate-able Labeled Transition Systems (LTS) from scenarios is also presented. Moreover, two deployment algorithms (a GA-based and an INLP-based) are implemented and the results are compared. [ABSTRACT FROM AUTHOR]

With the increasing power densities, managing the on-chip temperature has become an important design challenge, especially for hard real-time systems. This paper addresses the problem of minimizing the peak temperature under hard real-time constraints using a combination of dynamic voltage scaling and dynamic power management. We derive a closed-form formulation for the peak temperature and provide a genetic-algorithm-based approach to solve the problem. Our approach is evaluated with both simulations and real measurements with an Intel i5 processor. The evaluation results demonstrate the effectiveness of the proposed approach compared to related works in the literature. [ABSTRACT FROM AUTHOR]

Rabbit is a stream cipher proposed by M. Boesgaard et al., and has been selected into the final portfolio after three evaluation phases of the ECRYPT Stream Cipher Project (eSTREAM). So far only a few papers studied its security besides a series of white papers by the designers of Rabbit. Recently we presented a new idea to evaluate the security of a word-oriented stream cipher algorithm from a smaller data granularity instead of its original data granularity and applied it successfully to the stream cipher SOSEMANUK. In this work we apply the same idea to the Rabbit algorithm and analyze its security in resistance against the guess and determine attack from the view point of byte units. As a result, we present two new approaches of solving all xj,t+1's and gj,t's from the next-state function and the extraction scheme of Rabbit, whose complexities are 2166 and 2140.68 respectively, which are dramatically lower than those proposed by Lu et al. (2192 and 2174 resp.) at ISC 2008. Finally based on the above new results we propose a byte-based guess and determine attack on Rabbit, which only needs a small segment of known keystream to recover the whole internal state of Rabbit with time complexity 2242. Though the complexity of our attack is far higher than that of a brute force (2128), we believe that some new techniques adopted in this paper are of interest for future work on Rabbit. [ABSTRACT FROM AUTHOR]

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

Many practical systems, such as thermal system, economic system and electric power system, can be more accurately described by the fractional-order system rather than integer-order system. Therefore, it is an important topic to study the fractional-order system and estimate its parameters. The problem of parameter estimation is essentially a multi-dimensional parameter optimization problem. In this paper, according to the average value of position information, an improved Tent mapping and a piecewise mutation probability, a modified particle swarm optimization (MPSO) algorithm is presented to solve the parameter estimation problem. The performance of MPSO is tested with eight benchmark functions, which proves the effectiveness of the algorithm. Based on the double-dispersion Cole model, the proposed MPSO algorithm is used to estimate the parameters for the generated simulated datasets. Experimental results show that the MPSO algorithm for parameters identification of the Cole model is an effective and promising method with high accuracy and good robustness. [ABSTRACT FROM AUTHOR]

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and experts. The concept of data publishing faces a lot of security issues, indicating that when any trusted organization provides data to a third party, personal information need not be disclosed. Therefore, to maintain the privacy of the data, this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer (Genetic GWO) algorithm for which a C-mixture parameter is used. The C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints, such as the k -anonymity, l -diversity and the m -privacy. A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class size. The minimum value of the fitness ensures the maximum utility and the maximum privacy. Experimentation was carried out using the adult dataset, and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9, respectively. [ABSTRACT FROM AUTHOR]

Recent developments in 3D graphics technology have led to extensive processes on 3D meshes (e.g., compression, simplification, transmission and watermarking), these processes unavoidably cause the visual perceptual degradation of the 3D objects. The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality. Meanwhile, for the 3D objects that are observed on 2D screens by the users, it is reasonable to apply image metric to assess the distortion caused by mesh simplification. We attempt to explore the efficiency of image metric for assessing the visual fidelity of the simplified 3D model in this paper. For this purpose, several latest and most effective image metrics, 2D snapshots, number and pooling algorithms are involved in our study, and finally tested on the IEETA simplification database. The statistical data allow the researcher to select the optimal parameter for this image-based mesh visual quality assessment and provide a new perspective for the design and performance assessment of mesh simplification algorithms. [ABSTRACT FROM AUTHOR]

Propositionalization has been proven to be a very effective solution for multi-relational data mining problems. The approaches usually follow a two-step principle: transforming the relational data into a single, flat table and applying a propositional learning algorithm. During the transformation, the target table gets expanded by adding many new features summarizing the information of the non-target tables. Based on the used feature construction strategy, this leads to a table of very high dimensionality with a lot of irrelevant and/or redundant features that can negatively affect the predictive performance. In this paper, we propose a modification of the traditional two-step framework to overcome such problems. The proposed approach evaluates the features during the construction phase and reports only a subset of highly predictive features to the propositional learner. We present an implementation of this approach using a genetic algorithm to search for an optimal feature subset. Our experiments on a number of benchmark datasets suggest that the modified framework can help propositionalization methods to significantly improve their predictive performance. [ABSTRACT FROM AUTHOR]

In the field of target-based sentiment analysis, the deep neural model combining attention mechanism is a remarkable success. In current research, it is commonly seen that attention mechanism is combined with Long Short-Term Memory (LSTM) networks. However, such neural network-based architectures generally rely on complex computation and only focus on single target. In this paper, we propose a gated hierarchical LSTM (GH-LSTMs) model which combines regional LSTM and sentence-level LSTM via a gated operation for the task of target-based sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM. Furthermore, it is able to concentrate on the long-distance dependency of target in the whole sentence via a sentence-level LSTM. The final results of our experiments on multi-domain datasets of two languages from SemEval 2016 indicate that our approach yields better performance than Support Vector Machine (SVM) and several typical neural network models. A case study of some typical examples also makes a supplement to this conclusion. [ABSTRACT FROM AUTHOR]

An intruder of a company's network may use stolen login credentials to silently collect sensitive data. Such malicious user behavior is difficult to detect as long as it does not trigger access violation or data leak alert. In this paper, we propose to use an ensemble of three unsupervised anomaly detection algorithms, namely OCSVM, RNN and Isolation Forest, to detect abnormal user behavior patterns. Besides, an User Behavior Analytics (UBA) Platform is proposed to collect logs, extract features and conduct experiments. The experiment results indicate that our algorithm outperforms each individual algorithm with recall of 96.55% and precision of 91.24% on average, while both OCSVM and RNN suffer from anomalies in the training set, and i F o r e s t produces more false positives and false negatives in prediction. [ABSTRACT FROM AUTHOR]

Color transfer is to alter an image's color composition by reference to the color characteristics of another image. In this paper, we build a system called artistic coloring that realizes automatic color transfer from famous paintings. It properly extracts the wonderful color characteristics of famous paintings and applies them to color transfer. Specially, we investigate the traditional color theme extraction methods and find their deficiencies. Based on this, we quantify the processing of human painting to find the main colors in the color palette and propose an artistic balanced color theme extraction algorithm aimed specially at paintings. In the experiments, a user study is carried out to evaluate the artistic balanced extraction method. Our proposed method achieves the highest score 3.98 in a 5-point evaluation, which is 41% higher than traditional methods at most. We have successfully tested the artistic coloring system on lots of images with different painting styles. The results are natural and have the similar color characteristics with their corresponding reference paintings. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a novel method of data sample reduction that can be applied, in particular, to the classification of interval type imprecise information. Its concept is based on the sensitivity method, inspired by artificial neural networks, while the goal is to increase the number of apposite classifications, and, consequently, to increase calculation speed. As evident in this paper, the use of reduction algorithm eliminates the particular elements of all data sample patterns which have insignificant or negative influence on the correctness of classification. The methodology was tested on pseudo-random and real data, as well as by way of comparative analysis with similar task algorithms. The presented procedure was also tested for use in situations in which the data sample representing the individual classes had been obtained by the k-means clustering procedure. [ABSTRACT FROM AUTHOR]

The regards to widespread impact of distribution networks and ever increasing demand for electricity, some strategies must be devized in order to well operate the distribution networks. In this paper, to enhance the accountability of the power system and to improve the system performance parameters, simultaneous placement of renewable energy generation (REG) sources (e.g., wind, solar and dispatchable distributed generators (DGs)) and capacitors are investigated in a modified radial distribution network with considering ZIP loads. To enhance all network parameters simultaneously to the best possible condition multi-objective functions are proposed and solved using non-dominated sorting genetic algorithm (NSGA II). The employed objectives contain all economical, environmental and technical aspects of distribution network. One of the most important advantages of the proposed multi-objective formulation is that it obtains non-dominated solutions allowing the system operator (decision maker) to exercise his/her personal preference in selecting each of those solutions based on the operating conditions of the system and the costs. It is clear that the implementation of each non-dominated solution needs related costs according to the technology used and the system performance characteristics. However, there is a paucity of objective methodologies for ranking the obtained non-dominated solutions considering economical, environmental and technical aspects. So, in this paper, data envelopment analysis (DEA) is suggested for this purpose. In other words, in this paper, first NSGA II is applied to the siting and sizing problem, and then the obtained non-dominated solutions are prioritized by DEA. The significant advantage of using DEA is that there is no need to impose the decision maker's idea into the model and ranking is done based on the efficiencies of the non-dominated solutions. The most efficient solution is the one which has improved network parameters considerably and has lowest costs. So, using DEA gives a realistic view of solutions and the provided results are for all, not for a specific decision maker. To validate the effectiveness of the proposed scheme, the simulations are carried out on a modified test case 33-bus radial distribution network. [ABSTRACT FROM AUTHOR]

In this paper, we consider enumeration of geodesics on a polyhedron, where a geodesic means locally-shortest path between two points. Particularly, we consider the following preprocessing problem: given a point s on a polyhedral surface and a positive real number r , to build a data structure that enables, for any point t on the surface, to enumerate all geodesics from s to t whose length is less than r. First, we present a naive algorithm by removing the trimming process from the MMP algorithm (1987). Next, we present an improved algorithm which is practically more efficient on a non-convex polyhedron, in terms of preprocessing time and memory consumption. Moreover, we introduce a single-pair geodesic graph to succinctly encode a result of geodesic query. Lastly, we compare these naive and improved algorithms by some computer experiments. [ABSTRACT FROM AUTHOR]