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This field is completely dependent on the internal and external parameters of the cameras. Finally, the validity of the proposed method is examined by simulation and empirical results.

These results show that the localization error will be significantly decreased in the optimized camera configuration. Abstract Generally, the issue of quality assurance is a specific assurance in computer networks. The conventional computer networks with hierarchical structures that are used in organizations are formed using some nodes of Ethernet switches within a tree structure.

Open Flow is one of the main fundamental protocols Read More Generally, the issue of quality assurance is a specific assurance in computer networks. Open Flow is one of the main fundamental protocols of Software-defined networks SDNs and provides the direct access to and change in program of sending network equipment such as switches and routers, physically and virtually. Lack of an open interface in data sending program has led to advent of integrated and close equipment that are similar to CPU in current networks.

This study proposes a solution to reduce traffic using a correct placement of virtual machines while their security is maintained. The proposed solution is based on the moth-flame optimization, which has been evaluated. The obtained results indicate the priority of the proposed method. Abstract Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image.

To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network CNN -based model to benefit from the Read More Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image.

To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network CNN -based model to benefit from the impressive ability of CNN in the image processing tasks. To this end, we propose a deep-based model for automatic grayscale image colorization.

Harnessing from convolutional-based pre-trained models, we fuse three pre-trained models, VGG16, ResNet50, and Inception-v2, to improve the model performance.

The average of three model outputs is used to obtain more rich features in the model. The fused features are fed to an encoder-decoder network to obtain a color image from a grayscale input image.

We perform a step-by-step analysis of different pre-trained models and fusion methodologies to include a more accurate combination of these models in the proposed model. Results on LFW and ImageNet datasets confirm the effectiveness of our model compared to state-of-the-art alternatives in the field. Abstract Software effort estimation plays an important role in software project management, and analogy-based estimation ABE is the most common method used for this purpose.

ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects Read More Software effort estimation plays an important role in software project management, and analogy-based estimation ABE is the most common method used for this purpose.

A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation. The proposed approach was implemented and tested on two well-known datasets of software projects. The results showed that weighted ABE models provide more accurate and better effort estimates relative to unweighted ABE models and that the PSO-SA hybrid approach has led to better and more accurate results compared with the other weighting approaches in both datasets.

Abstract For an economic review of food prices in May to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time.

The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical Read More For an economic review of food prices in May to determine the trend of rising or decreasing prices compared to previous periods, we considered the price of food items at that time.

The types of items consumed during specific periods in urban areas and the whole country are selected for our statistical analysis. Among the various methods of modelling and statistical prediction, and in a new approach, we modeled the data using data mining techniques consisting of decision tree methods, associative rules, and Bayesian law.

Then, prediction, validation, and standardization of the accuracy of the validation are performed on them. Results of data validation in the urban and national area and the results of the standardization of the accuracy of validation in the urban and national area are presented with the desired accuracy.

Most of the methods of detecting breast cancer are very expensive and difficult to interpret such as mammography. There are also limitations such as cumulative radiation exposure, over-diagnosis, false There are also limitations such as cumulative radiation exposure, over-diagnosis, false positives and negatives in women with a dense breast which pose certain uncertainties in high-risk population.

This will serve as a complement to these expensive methods. High ranking features were extracted from the dataset. Furthermore, fold cross validation and holdout procedures were used coupled with changing of random seed.

The result showed that KNN algorithm has the highest and best accuracy of This is followed by the J48 with SVM had Although it was also discovered that Blood Glucose level is a major determinant in detecting breast cancer, it has to be combined with other attributes to make decision as a result of other health issues like diabetes.

With the result obtained, women are advised to do regular check-ups including blood analysis in order to know which of the blood components need to be worked on to prevent breast cancer based on the model generated in this study. Abstract Background: One of the most important concepts in cloud computing is modeling the problem as a multi-layer optimization problem which leads to cost savings in designing and operating the networks.

Previous researchers have modeled the two-layer network operating problem as an Integer Linear Programming Read More Background: One of the most important concepts in cloud computing is modeling the problem as a multi-layer optimization problem which leads to cost savings in designing and operating the networks. Previous researchers have modeled the two-layer network operating problem as an Integer Linear Programming ILP problem, and due to the computational complexity of solving it jointly, they suggested a two-stage procedure for solving it by considering one layer at each stage.

Aim: In this paper, considering the ILP model and using some properties of it, we propose a heuristic algorithm for solving the model jointly, considering unicast, multicast, and anycast flows simultaneously. Method: We first sort demands in decreasing order and use a greedy method to realize demands in order.

Conclusion: The proposed greedy algorithm could solve the large-scale networks approximately in polynomial time and its approximation is reasonable. Abstract Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult.

So, the development of MOEAs will always Read More Many of the real-world issues have multiple conflicting objectives that the optimization between contradictory objectives is very difficult. So, the development of MOEAs will always lead to the advancement of science. ASMOGA uses the archive technique to save the history of the search procedure, so that the maintenance of the diversity in the decision space is satisfied adequately.

The optimization results are evaluated by Performance Metrics such as hypervolume, Generational Distance, Spacing, and the t-test a statistical test ; based on the results, the superiority of the proposed algorithm is identified clearly. Review Article. Abstract Battery power limitation of sensor nodes SNs is a major challenge for wireless sensor networks WSNs which affects network survival.

Thus, optimizing the energy consumption of the SNs as well as increasing the lifetime of the SNs and thus, extending the lifetime of WSNs are of crucial importance in Read More Battery power limitation of sensor nodes SNs is a major challenge for wireless sensor networks WSNs which affects network survival. Thus, optimizing the energy consumption of the SNs as well as increasing the lifetime of the SNs and thus, extending the lifetime of WSNs are of crucial importance in these types of networks.

Then, forwarding issues the MC has been considered in the role of power transmitter in WSNs and the existing approaches are categorized, with the purposes and limitations of MC dispatching studied. Then an overview of the existing articles is presented and to better understand the contents, tables and figures are offered that summarize the existing methods. We examine them in different dimensions such as advantages and disadvantages etc.

Finally, the future prospects of MC are discussed. Abstract Wireless body area networks WBAN are innovative technologies that have been the anticipation greatly promote healthcare monitoring systems.

All WBAN included biomedical sensors that can be worn on or implanted in the body. Sensors are monitoring vital signs and then processing the data and transmitting Read More Wireless body area networks WBAN are innovative technologies that have been the anticipation greatly promote healthcare monitoring systems.

Sensors are monitoring vital signs and then processing the data and transmitting to the central server. Biomedical sensors are limited in energy resources and need an improved design for managing energy consumption. The proposed method uses fuzzy logic based on the distance to sink and the remaining energy and length of data to select the cluster head.

Results of the simulations indicate that it works better to extend the lifetime and guarantee minimum energy and packet delivery rates, maximizing the throughput. Abstract This paper presents a comprehensive review of the works done, during the —, in the application of data mining techniques in Credit scoring.

Using a novel research approach, this paper investigates Read More This paper presents a comprehensive review of the works done, during the —, in the application of data mining techniques in Credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The articles are categorized and classified into enterprise, individual and small and midsized SME companies credit scoring.

Data mining techniques is also categorized to single classifier, Hybrid methods and Ensembles. The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Paper analysis provides a guide to future researches and concludes with several suggestions for further studies.

Image Processing and Computer Vision. Abstract The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras.

A lot of multi-focus image fusion techniques have been introduced Read More The purpose of multi-focus image fusion is gathering the essential information and the focused parts from the input multi-focus images into a single image. A lot of multi-focus image fusion techniques have been introduced using considering the focus measurement in the spatial domain.

However, the multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform DCT domain, especially when JPEG images are used in visual sensor networks VSN.

So the most of the researchers are interested in focus measurements calculation and fusion processes directly in DCT domain.

Accordingly, many researchers developed some techniques which are substituting the spatial domain fusion process with DCT domain fusion process.

Previous works in DCT domain have some shortcomings in selection of suitable divided blocks according to their criterion for focus measurement. In addition, two other new focus measurements which work by measuring correlation coefficient between source blocks and artificial blurred blocks are developed completely in DCT domain.

However, a new consistency verification method is introduced as a post-processing, improving the quality of fused image significantly. These proposed methods reduce the drawbacks significantly due to unsuitable block selection. The output images quality of our proposed methods is demonstrated by comparing the results of proposed algorithms with the previous algorithms. Abstract Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks WSNs.

To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient Read More Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks WSNs. To address the problem of outlier detection in wireless sensor networks, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN.

The outliers in sensed data can be caused due to compromised or malfunctioning nodes. In the distributed approach, we use distributed principal component analysis DPCA and fixed-width clustering FWC in order to establish a global normal pattern and to detect outlier. The process of establishing the global normal pattern is distributed among all sensor nodes.

We also use weighted coefficients and a forgetting curve to periodically update the established normal profile.

We demonstrate that the proposed distributed approach achieves comparable accuracy compared to the centralized approach, while the communication overhead in the network and energy consumption is significantly reduced. Abstract Quality of service QoS is an important issue in the design and management of web service composition.

QoS in web services consists of various non-functional factors, such as execution cost, execution time, availability, successful execution rate, and security. In recent years, the number of available Poria, D. Hazarika, N. Majumder, G. Naik, E. Cambria, R. Chen, S. It contains the same dialogue instances available in EmoryNLP Emotion Detection dataset, but it also encompasses audio and visual modality along with text.

Each utterance in a dialogue has been labeled by any of these seven emotions -- Neutral, Joyful, Peaceful, Powerful, Scared, Mad and Sad. The annotations are borrowed from the original dataset. Video clips of this dataset can be download from this link. There are 3. Each entry in the first column of these csv files contain an utterance whose corresponding video clip can be found here. Each utterance and its video clip is indexed by the season no. A scene is simply a dialogue. This indexing is consistent with the original dataset.

Note : There are a few utterances for which we were not able to find the start and end time due to some inconsistencies in the subtitles. Such utterances have been omitted from the dataset. However, we encourage the users to find the corresponding utterances from the original dataset and generate video clips for the same. Zahiri and J. Each utterance is accompanied by its context, which provides additional information on the scenario where the utterance occurs.

Example sarcastic utterance from the dataset along with its context and transcript. We provide a Google Drive folder with the raw video clips , including both the utterances and their respective context. Each instance in the JSON file is allotted one identifier e. Evaluation: We evaluate using weighted F-score metric in a 5-fold cross validation scheme.

Refer to our baseline scripts for more details. We make groups of these samples utterances based on their context into scenes. Each utterance in each scene consists of a label indicating humor of that utterance i. Besides, each utterance is also annotated with its speaker and listener information. In multiparty conversation, listener identification poses a great challenge. In our dataset, we define the listener as the party in the conversation to whom the speaker is replying.

Each utterance in each scene is coupled with its context utterances, which are preceding turns by the speakers participating in the conversation. It also contains multi-party conversations that are more challenging to classify than dyadic variants. It also contains the manually annotated labels of the utterances. Using the Episode id and scene id, one can map the utterances in the Raw-Text folder to the corresponding audio and visual segments in Raw-Audio and Raw-Visual.

This should result in multimodal data. The Label column in the TSV files e. The text data are stored in TSV format. The text data contains the following fields:. Every episode has a dedicated folder e. For every episode, each scene has a dedicated folder e. Skip to content. Star MIT License. Branches Tags. Could not load branches. Could not load tags. Latest commit.

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