A. Hashemi; M. A. Zare Chahooki
Abstract
Social networks are valuable sources for marketers. Marketers can publish campaigns to reach target audiences according to their interest. Although Telegram was primarily designed as an instant messenger, it is used as a social network in Iran due to censorship of Facebook, Twitter, etc. Telegram neither ...
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Social networks are valuable sources for marketers. Marketers can publish campaigns to reach target audiences according to their interest. Although Telegram was primarily designed as an instant messenger, it is used as a social network in Iran due to censorship of Facebook, Twitter, etc. Telegram neither provides a marketing platform nor the possibility to search among groups. It is difficult for marketers to find target audience groups in Telegram, hence we developed a system to fill the gap. Marketers use our system to find target audience groups by keyword search. Our system has to search and rank groups as relevant as possible to the search query. This paper proposes a method called GroupRank to improve the ranking of group searching. GroupRank elicits associative connections among groups based on membership records they have in common. After detailed analysis, five-group quality factors have been introduced and used in the ranking. Our proposed method combines TF-IDF scoring with group quality scores and associative connections among groups. Experimental results show improvement in many different queries.
M. Kakooei; Y. Baleghi
Abstract
Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very ...
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Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very High Resolution (VHR) imagery provides more detailed data from objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to automatically determine shadow and solar illumination direction in VHR data. The proposed feature is based on inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in remote sensing metadata. Shadow intensity is supposed to be dependent on two factors, including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on VHR data with 0.3m resolution. Finally, SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.
S. Ahmadluei; K. Faez; B. Masoumi
Abstract
Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there ...
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Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there is no standard methodology for designing the CNN architecture for the intended problem. Network pruning/compression has emerged as a research direction to address the first challenge, and it has proven to moderate CNN computational load successfully. For the second challenge, various evolutionary algorithms have been proposed thus far. The algorithm proposed in this paper can be viewed as a solution to both challenges. Instead of using constant predefined criteria to evaluate the filters of CNN layers, the proposed algorithm establishes evaluation criteria in online manner during network training based on the combination of each filter’s profit in its layer and the next layer. In addition, the novel method suggested that it inserts new filters into the CNN layers. The proposed algorithm is not simply a pruning strategy but determines the optimal number of filters. Training on multiple CNN architectures allows us to demonstrate the efficacy of our approach empirically. Compared to current pruning algorithms, our algorithm yields a network with a remarkable prune ratio and accuracy. Despite the relatively high computational cost of an epoch in the proposed algorithm in pruning, altogether it achieves the resultant network faster than other algorithms.
S. Shadravan; H. Naji; V. Khatibi
Abstract
The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm ...
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The SailFish Optimizer (SFO) is a metaheuristic algorithm inspired by a group of hunting sailfish that alternates their attacks on group of prey. The SFO algorithm takes advantage of using a simple method for providing the dynamic balance between exploration and exploitation phases, creating the swarm diversity, avoiding local optima, and guaranteeing high convergence speed. Nowadays, multi agent systems and metaheuristic algorithms can provide high performance solutions for solving combinatorial optimization problems. These methods provide a prominent approach to reduce the execution time and improve of the solution quality. In this paper, we elaborate a multi agent based and distributed method for sailfish optimizer (DSFO), which improves the execution time and speedup of the algorithm while maintaining the results of optimization in high quality. The Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) are used for the massive computation requirements in this approach. In depth of the study, we present the implementation details and performance observations of DSFO algorithm. Also, a comparative study of distributed and sequential SFO is performed on a set of standard benchmark optimization functions. Moreover, the execution time of distributed SFO is compared with other parallel algorithms to show the speed of the proposed algorithm for solving unconstrained optimization problems. The final results indicate that the proposed method is executed about maximum 14 times faster than other parallel algorithms and shows the ability of DSFO for solving non-separable, non-convex and scalable optimization problems.
H. Fathi; A.R. Ahmadyfard; H. Khosravi
Abstract
Recently, significant attention has been paid to the development of virtual reality systems in several fields such as commerce. Trying on virtual clothes is becoming a solution for the online clothing industry. In this paper, we propose a method for the problem of virtual clothing using 3D point matching ...
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Recently, significant attention has been paid to the development of virtual reality systems in several fields such as commerce. Trying on virtual clothes is becoming a solution for the online clothing industry. In this paper, we propose a method for the problem of virtual clothing using 3D point matching of a selected cloth and the customer body. For this purpose, we provide a 3D model of the customer and the selected clothes, put up on the mannequin, using a Kinect camera. As the size of the abdominal part of the customer is different from the mannequin, after pre-processing of the two captured point clouds, the 3D point cloud of the selected clothes is deformed to fit the 3D point cloud of the customer’s body. We use Laplacian-Beltrami curvature as a descriptor to find the abdominal part in the two point clouds. Then, the abdominal part of the mannequin is deformed in 3D space to fit the abdominal part of the customer. Finally, the head and neck of the customer are attached to the mannequin point.The proposed method has two main advantages over the existing methods for virtual clothing. First, no need for an expert to design a 3D model for the customer body and the selected clothes in advanced graphical software such as Unity. Second, there is no restriction for the style of the selected clothes and their texture while existing methods have such restrictions. The experimental results justify the ability of the proposed method for virtual clothing.
Z. MohammadHosseini; A. Jalaly Bidgoly
Abstract
Social media is an inseparable part of human life, although published information through social media is not always true. Rumors may spread easily and quickly in the social media, hence, it is vital to have a tool for rumor veracity detection. Papers already proved that users’ stance is an important ...
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Social media is an inseparable part of human life, although published information through social media is not always true. Rumors may spread easily and quickly in the social media, hence, it is vital to have a tool for rumor veracity detection. Papers already proved that users’ stance is an important tool for this goal. To the best knowledge of authors, so far, no work has been proposed to study the ordering of users’ stances to achieve the best possible accuracy. In this work, we have investigated the importance of the stances ordering in the efficiency of rumor veracity detection. This paper introduces a concept called trust for stance sequence ordering and shows that proper definition of this function can significantly help improve to improve veracity detection. The paper examines and compares different modes of definition of trust. Then, by choosing the best possible definition, it was able to outperform state-of-the-art results on a well-known dataset in this field, namely SemEval 2019.
M. Yadollahzadeh Tabari; Z. Mataji
Abstract
The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. ...
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The Internet of Things (IoT) is a novel paradigm in computer networks which is capable to connect things to the internet via a wide range of technologies. Due to the features of the sensors used in IoT networks and the unsecured nature of the internet, IoT is vulnerable to many internal routing attacks. Using traditional IDS in these networks has its own challenges due to the resource constraint of the nodes, and the characteristics of the IoT network. A sinkhole attacker node, in this network, attempts to attract traffic through incorrect information advertisement. In this research, a distributed IDS architecture is proposed to detect sinkhole routing attack in RPL-based IoT networks, which is aimed to improve true detection rate and reduce the false alarms. For the latter we used one type of post processing mechanism in which a threshold is defined for separating suspicious alarms for further verifications. Also, the implemented IDS modules distributed via client and router border nodes that makes it energy efficient. The required data for interpretation of network’s behavior gathered from scenarios implemented in Cooja environment with the aim of Rapidminer for mining the produces patterns. The produced dataset optimized using Genetic algorithm by selecting appropriate features. We investigate three different classification algorithms which in its best case Decision Tree could reaches to 99.35 rate of accuracy.
M. Sepahvand; F. Abdali-Mohammadi
Abstract
The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local ...
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The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local features or sequences of them. Whereas, it has been shown that the combination of global and local features can increase the recognition accuracy. This paper addresses two mentioned topics. First, a new high discriminative local feature, called Rotation Invariant Histogram of Degrees (RIHoD), is proposed for online digitizer-pen handwriting signals. Second, a feature representation layer is proposed, which maps local features into global ones in a new space using some learning kernels. Different aspects of the proposed local feature and learned global feature are analyzed and its efficiency is evaluated in several online handwriting recognition scenarios.
M. Rahimi; A. A. Taheri; H. Mashayekhi
Abstract
Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. ...
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Finding an effective way to combine the base learners is an essential part of constructing a heterogeneous ensemble of classifiers. In this paper, we propose a framework for heterogeneous ensembles, which investigates using an artificial neural network to learn a nonlinear combination of the base classifiers. In the proposed framework, a set of heterogeneous classifiers are stacked to produce the first-level outputs. Then these outputs are augmented using several combination functions to construct the inputs of the second-level classifier. We conduct a set of extensive experiments on 121 datasets and compare the proposed method with other established and state-of-the-art heterogeneous methods. The results demonstrate that the proposed scheme outperforms many heterogeneous ensembles, and is superior compared to singly tuned classifiers. The proposed method is also compared to several homogeneous ensembles and performs notably better. Our findings suggest that the improvements are even more significant on larger datasets.
A. Lakizadeh; Z. Zinaty
Abstract
Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to ...
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Aspect-level sentiment classification is an essential issue in sentiment analysis that intends to resolve the sentiment polarity of a specific aspect mentioned in the input text. Recent methods have discovered the role of aspects in sentiment polarity classification and developed various techniques to assess the sentiment polarity of each aspect in the text. However, these studies do not pay enough attention to the need for vectors to be optimal for the aspect. To address this issue, in the present study, we suggest a Hierarchical Attention-based Method (HAM) for aspect-based polarity classification of the text. HAM works in a hierarchically manner; firstly, it extracts an embedding vector for aspects. Next, it employs these aspect vectors with information content to determine the sentiment of the text. The experimental findings on the SemEval2014 data set show that HAM can improve accuracy by up to 6.74% compared to the state-of-the-art methods in aspect-based sentiment classification task.
F. Baratzadeh; Seyed M. H. Hasheminejad
Abstract
With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. ...
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With the advancement of technology, the daily use of bank credit cards has been increasing exponentially. Therefore, the fraudulent use of credit cards by others as one of the new crimes is also growing fast. For this reason, detecting and preventing these attacks has become an active area of study. This article discusses the challenges of detecting fraudulent banking transactions and presents solutions based on deep learning. Transactions are examined and compared with other traditional models in fraud detection. According to the results obtained, optimal performance is related to the combined model of deep convolutional networks and short-term memory, which is trained using the aggregated data received from the generative adversarial network. This paper intends to produce sensible data to address the unequal class distribution problem, which is far more effective than traditional methods. Also, it uses the strengths of the two approaches by combining deep convolutional network and Long Short Term Memory network to improve performance. Due to the inefficiency of evaluation criteria such as accuracy in this application, the measure of distance score and the equal error rate has been used to evaluate models more transparent and more precise. Traditional methods were compared to the proposed approach to evaluate the efficiency of the experiment.
M. M. Jaziriyan; F. Ghaderi
Abstract
Most of the existing neural machine translation (NMT) methods translate sentences without considering the context. It is shown that exploiting inter and intra-sentential context can improve the NMT models and yield to better overall translation quality. However, providing document-level data is costly, ...
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Most of the existing neural machine translation (NMT) methods translate sentences without considering the context. It is shown that exploiting inter and intra-sentential context can improve the NMT models and yield to better overall translation quality. However, providing document-level data is costly, so properly exploiting contextual data from monolingual corpora would help translation quality. In this paper, we proposed a new method for context-aware neural machine translation (CA-NMT) using a combination of hierarchical attention networks (HAN) and automatic post-editing (APE) techniques to fix discourse phenomena when there is lack of context. HAN is used when we have a few document-level data, and APE can be trained on vast monolingual document-level data to improve results further. Experimental results show that combining HAN and APE can complement each other to mitigate contextual translation errors and further improve CA-NMT by achieving reasonable improvement over HAN (i.e., BLEU score of 22.91 on En-De news-commentary dataset).
A. Omondi; I. Lukandu; G. Wanyembi
Abstract
Variable environmental conditions and runtime phenomena require developers of complex business information systems to expose configuration parameters to system administrators. This allows system administrators to intervene by tuning the bottleneck configuration parameters in response to current changes ...
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Variable environmental conditions and runtime phenomena require developers of complex business information systems to expose configuration parameters to system administrators. This allows system administrators to intervene by tuning the bottleneck configuration parameters in response to current changes or in anticipation of future changes in order to maintain the system’s performance at an optimum level. However, these manual performance tuning interventions are prone to error and lack of standards due to fatigue, varying levels of expertise and over-reliance on inaccurate predictions of future states of a business information system. As a result, the purpose of this research is to investigate on how the capacity of probabilistic reasoning to handle uncertainty can be combined with the capacity of Markov chains to map stochastic environmental phenomena to ideal self-optimization actions. This was done using a comparative experimental research design that involved quantitative data collection through simulations of different algorithm variants. This provided compelling results that indicate that applying the algorithm in a distributed database system improves performance of tuning decisions under uncertainty. The improvement was quantitatively measured by a response-time latency that was 27% lower than average and a transaction throughput that was 17% higher than average.
H.R. Koosha; Z. Ghorbani; R. Nikfetrat
Abstract
In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are ...
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In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are of great importance, especially in today's hectic world, which search for content that would be interesting to an individual. In this research, a new two-steps recommender system is proposed based on demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naïve Bayesian Classification. The results are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results showed the effectiveness of the K-means/FA/KNN compared with other extant models.
H.3. Artificial Intelligence
M. Taghian; A. Asadi; R. Safabakhsh
Abstract
The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved ...
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The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with deep reinforcement learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
M. Zarbazoo Siahkali; A.A. Ghaderi; Abdol H. Bahrpeyma; M. Rashki; N. Safaeian Hamzehkolaei
Abstract
Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate ...
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Scouring, occurring when the water flow erodes the bed materials around the bridge pier structure, is a serious safety assessment problem for which there are many equations and models in the literature to estimate the approximate scour depth. This research is aimed to study how surrogate models estimate the scour depth around circular piers and compare the results with those of the empirical formulations. To this end, the pier scour depth was estimated in non-cohesive soils based on a subcritical flow and live bed conditions using the artificial neural networks (ANN), group method of data handling (GMDH), multivariate adaptive regression splines (MARS) and Gaussian process models (Kriging). A database containing 246 lab data gathered from various studies was formed and the data were divided into three random parts: 1) training, 2) validation and 3) testing to build the surrogate models. The statistical error criteria such as the coefficient of determination (R2), root mean squared error (RMSE), mean absolute percentage error (MAPE) and absolute maximum percentage error (MPE) of the surrogate models were then found and compared with those of the popular empirical formulations. Results revealed that the surrogate models’ test data estimations were more accurate than those of the empirical equations; Kriging has had better estimations than other models. In addition, sensitivity analyses of all surrogate models showed that the pier width’s dimensionless expression (b/y) had a greater effect on estimating the normalized scour depth (Ds/y).
M. Khanzadi; H. Veisi; R. Alinaghizade; Z. Soleymani
Abstract
One of the main problems in children with learning difficulties is the weakness of phonological awareness (PA) skills. In this regard, PA tests are used to evaluate this skill. Currently, this assessment is paper-based for the Persian language. To accelerate the process of the assessments and make it ...
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One of the main problems in children with learning difficulties is the weakness of phonological awareness (PA) skills. In this regard, PA tests are used to evaluate this skill. Currently, this assessment is paper-based for the Persian language. To accelerate the process of the assessments and make it engaging for children, we propose a computer-based solution that is a comprehensive Persian phonological awareness assessment system implementing expressive and pointing tasks. For the expressive tasks, the solution is powered by recurrent neural network-based speech recognition systems. To this end, various recognition modules are implemented, including a phoneme recognition system for the phoneme segmentation task, a syllable recognition system for the syllable segmentation task, and a sub-word recognition system for three types of phoneme deletion tasks, including initial, middle, and final phoneme deletion. The recognition systems use bidirectional long short-term memory neural networks to construct acoustic models. To implement the recognition systems, we designed and collected Persian Kid’s Speech Corpus that is the largest in Persian for children’s speech. The accuracy rate for phoneme recognition was 85.5%, and for syllable recognition was 89.4%. The accuracy rates of the initial, middle, and final phoneme deletion were 96.76%, 98.21%, and 95.9%, respectively.
P. Abdzadeh; H. Veisi
Abstract
Automatic Speaker Verification (ASV) systems have proven to bevulnerable to various types of presentation attacks, among whichLogical Access attacks are manufactured using voiceconversion and text-to-speech methods. In recent years, there has beenloads of work concentrating on synthetic speech detection, ...
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Automatic Speaker Verification (ASV) systems have proven to bevulnerable to various types of presentation attacks, among whichLogical Access attacks are manufactured using voiceconversion and text-to-speech methods. In recent years, there has beenloads of work concentrating on synthetic speech detection, and with the arrival of deep learning-based methods and their success in various computer science fields, they have been a prevailing tool for this very task too. Most of the deep neural network-based techniques forsynthetic speech detection have employed the acoustic features basedon Short-Term Fourier Transform (STFT), which are extracted from theraw audio signal. However, lately, it has been discovered that the usageof Constant Q Transform's (CQT) spectrogram can be a beneficialasset both for performance improvement and processing power andtime reduction of a deep learning-based synthetic speech detection. In this work, we compare the usage of the CQT spectrogram and some most utilized STFT-based acoustic features. As lateral objectives, we consider improving the model's performance as much as we can using methods such as self-attention and one-class learning. Also, short-duration synthetic speech detection has been one of the lateral goals too. Finally, we see that the CQT spectrogram-based model not only outperforms the STFT-based acoustic feature extraction methods but also reduces the processing time and resources for detecting genuine speech from fake. Also, the CQT spectrogram-based model places wellamong the best works done on the LA subset of the ASVspoof 2019 dataset, especially in terms of Equal Error Rate.
R. Ghotboddini; H. Toossian Shandiz
Abstract
Lighting continuity is one of the preferences of citizens. Public lighting management from the viewpoint of city residents improves social welfare. The quality of lamps and duration of lighting defect correction is important in lighting continuity. In this regard, reward and penalty mechanism plays an ...
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Lighting continuity is one of the preferences of citizens. Public lighting management from the viewpoint of city residents improves social welfare. The quality of lamps and duration of lighting defect correction is important in lighting continuity. In this regard, reward and penalty mechanism plays an important role in contract. Selecting labor and lamps has a significant impact on risk reduction during the contract period. This research improves strategies for public lighting asset management. The lifespan of lamp that announced by manufacturers is used to calculate maintenance cost in order to provide a possibility to estimate the actual cost of high-pressure sodium luminaire in public lighting system. Guarantee period of lamps and maximum permissible lighting defect detection and correction time is used for reward and penalty mechanism. The result shows that the natural life guarantee and permissible correction time have a considerable effect in maintenance cost and city resident’s satisfaction.
A. Fakhari; K. Kiani
Abstract
Image restoration and its different variations are important topics in low-level image processing. One of the main challenges in image restoration is dependency of current methods to the corruption characteristics. In this paper, we have proposed an image restoration architecture that enables us to address ...
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Image restoration and its different variations are important topics in low-level image processing. One of the main challenges in image restoration is dependency of current methods to the corruption characteristics. In this paper, we have proposed an image restoration architecture that enables us to address different types of corruption, regardless of type, amount and location. The main intuition behind our approach is restoring original images from abstracted perceptual features. Using an encoder-decoder architecture, image restoration can be defined as an image transformation task. Abstraction of perceptual features is done in the encoder part of the model and determines the sampling point within original images' Probability Density Function (PDF). The PDF of original images is learned in the decoder section by using a Generative Adversarial Network (GAN) that receives the sampling point from the encoder part. Concretely, sampling from the learned PDF restores original image from its corrupted version. Pretrained network extracts perceptual features and Restricted Boltzmann Machine (RBM) makes the abstraction over them in the encoder section. By developing a new algorithm for training the RBM, the features of the corrupted images have been refined. In the decoder, the Generator network restores original images from abstracted perceptual features while Discriminator determines how good the restoration result is. The proposed approach has been compared with both traditional approaches like BM3D and with modern deep models like IRCNN and NCSR. We have also considered three different categories of corruption including denoising, inpainting and deblurring. Experimental results confirm performance of the model.
Seyed Mahdi Sadatrasoul; Omid Mahdi Ebadati; Amir Amirzadeh Irani
Abstract
Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s ...
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Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than 20% change in adjusting company’s financial statements and balance sheets parameters.
A. Lakizadeh; E. Moradizadeh
Abstract
Text sentiment classification in aspect level is one of the hottest research topics in the field of natural language processing. The purpose of the aspect-level sentiment analysis is to determine the polarity of the text according to a particular aspect. Recently, various methods have been developed ...
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Text sentiment classification in aspect level is one of the hottest research topics in the field of natural language processing. The purpose of the aspect-level sentiment analysis is to determine the polarity of the text according to a particular aspect. Recently, various methods have been developed to determine sentiment polarity of the text at the aspect level, however, these studies have not yet been able to model well complementary effects of the context and aspect in the polarization detection process. Here, we present ACTSC, a method for determining the sentiment polarity of the text based on separate embedding of aspects and context. In the first step, ACTSC deals with separate modelling of the aspects and context to extract new representation vectors. Next, by combining generative representations of aspect and context, it determines the corresponding polarity to each particular aspect using a short-term memory network and a self-attention mechanism. Experimental results in the SemEval2014 dataset in both restaurant and laptop categories show that ACTSC has been able to improve the accuracy of aspect-based sentiment classification compared to the latest proposed methods.
H. Sadr; Mir M. Pedram; M. Teshnehlab
Abstract
With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted ...
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With the rapid development of textual information on the web, sentiment analysis is changing to an essential analytic tool rather than an academic endeavor and numerous studies have been carried out in recent years to address this issue. By the emergence of deep learning, deep neural networks have attracted a lot of attention and become mainstream in this field. Despite the remarkable success of deep learning models for sentiment analysis of text, they are in the early steps of development and their potential is yet to be fully explored. Convolutional neural network is one of the deep learning methods that has been surpassed for sentiment analysis but is confronted with some limitations. Firstly, convolutional neural network requires a large number of training data. Secondly, it assumes that all words in a sentence have an equal contribution to the polarity of a sentence. To fill these lacunas, a convolutional neural network equipped with the attention mechanism is proposed in this paper which not only takes advantage of the attention mechanism but also utilizes transfer learning to boost the performance of sentiment analysis. According to the empirical results, our proposed model achieved comparable or even better classification accuracy than the state-of-the-art methods.
H. Morshedlou; A.R. Tajari
Abstract
Edge computing is an evolving approach for the growing computing and networking demands from end devices and smart things. Edge computing lets the computation to be offloaded from the cloud data centers to the network edge for lower latency, security, and privacy preservation. Although energy efficiency ...
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Edge computing is an evolving approach for the growing computing and networking demands from end devices and smart things. Edge computing lets the computation to be offloaded from the cloud data centers to the network edge for lower latency, security, and privacy preservation. Although energy efficiency in cloud data centers has been widely studied, energy efficiency in edge computing has been left uninvestigated. In this paper, a new adaptive and decentralized approach is proposed for more energy efficiency in edge environments. In the proposed approach, edge servers collaborate with each other to achieve an efficient plan. The proposed approach is adaptive, and consider workload status in local, neighboring and global areas. The results of the conducted experiments show that the proposed approach can improve energy efficiency at network edges. e.g. by task completion rate of 100%, the proposed approach decreases energy consumption of edge servers from 1053 Kwh to 902 Kwh.
H. Kalani; E. Abbasi
Abstract
Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics ...
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Posterior crossbite is a common malocclusion disorder in the primary dentition that strongly affects masticatory function. To the best of the author’s knowledge, for the first time, this article presents a reasonable and computationally efficient diagnostic system for detecting characteristics between children with and without unilateral posterior crossbite (UPCB) in the primary dentition from the surface electromyography (sEMG) activity of masticatory muscles. In this study, 40 children (4–6y) were selected and divided into UPCB (n = 20) and normal occlusion (NOccl; n = 20) groups. The preferred chewing side was determined using a visual spot-checking method. The chewing rate was determined as the average of two chewing cycles. The sEMG activity of the bilateral masticatory muscles was recorded during two 20-s gum-chewing sequences. The data of the subjects were diagnosed by the dentist. In this study, the fast Fourier transform (FFT) analysis was applied to sEMG signals recorded from subjects. The number of FFT coefficients had been selected by using Logistic Regression (LR) methodology. Then the ability of a multilayer perceptron artificial neural network (MLPANN) in the diagnosis of neuromuscular disorders in investigated. To find the best neuron weights and structures for MLPANN, particle swarm optimization (PSO) was utilized. Results showed the proficiency of the suggested diagnostic system for the classification of EMG signals. The proposed method can be utilized in clinical applications for diagnoses of unilateral posterior crossbite.
