ACM Transactions on Asian and Low-Resource Language Information Processing
Social media platforms have made increasing use of irony in recent years. Users can express their... more Social media platforms have made increasing use of irony in recent years. Users can express their ironic thoughts with audio, video, and images attached to text content. When you use irony, you are making fun of a situation or trying to make a point. It can also express frustration or highlight the absurdity of a situation. The use of irony in social media is likely to continue to increase, no matter the reason. By using syntactic information in conjunction with semantic exploration, we show that attention networks can be enhanced. Using learned embedding, unsupervised learning encodes word order into a joint space. By evaluating the entropy of an example class and adding instances, the active learning method uses the shared representation as a query to retrieve semantically similar sentences from a knowledge base. In this way, the algorithm can identify the instance with the maximum uncertainty and extract the most informative example from the training set. An ironic network traine...
A serious issue in today’s society is Depression which can have a devastating impact on a person’... more A serious issue in today’s society is Depression which can have a devastating impact on a person’s ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, we decided to investigate the language used in individuals’ personal expressions to identify depressive symptoms via social media. Graph Attention Networks (GATs) are used in this study as a solution to the problems associated with text classification of depression. These GATs can be constructed using masked self-attention layers. Rather than requiring expensive matrix operations such as similarity or knowledge of network architecture, this study implicitly assigns weights to each node in a neighbourhood. This is possible because nodes and words can carry the properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the...
ACM Transactions on Asian and Low-Resource Language Information Processing
With the increasing use of online mediums, Internet-delivered psychological treatments (IDPs) are... more With the increasing use of online mediums, Internet-delivered psychological treatments (IDPs) are becoming an essential tool for improving mental disorders. Online-based health therapies can help a large segment of the population with little resource investment. The task is greatly complicated by the overlapping emotions for specific mental health. Early adoption of a deep learning system presented severe difficulties, including ethical and legal considerations that contributed to a lack of trust. Modern models required highly interpretable, intuitive explanations that humans could understand. To achieve this, we present a deep attention model based on fuzzy classification that uses the linguistic features of patient texts to build emotional lexicons. In medical applications, a diversified dataset generates work. Active learning techniques are used to extend fuzzy rules and the learned dataset gradually. From this, the model can gain a reduction in labeling efforts in mental health ...
In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatm... more In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attenti...
ACM Transactions on Asian and Low-Resource Language Information Processing, 2022
Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet int... more Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes...
Effective vector representation has been proven useful for transaction classification and cluster... more Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the resu...
High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utili... more High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insert...
Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs an... more Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, fe...
Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, a... more Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achie...
International Journal of Data Warehousing and Mining, 2016
In this paper, the authors propose an approach and different tools to evaluate the performance an... more In this paper, the authors propose an approach and different tools to evaluate the performance and assess the effectiveness of a model in the field of dynamic cubing. Experimental evaluation, on one hand allows observing the behavior and the performance of the solution, while on the other hand it lets one compare the results with those of the other competing solutions. The authors' proposal includes an experimental workflow based on a set of configuration parameters to characterize the inputs (data sets, queries sets and algorithm input parameters) and a set of metrics to analyze and qualify the output (performance and behavior metrics) of the solution. They have identified a number of useful tools necessary to develop an experimental evaluation strategy. These monitoring tools allow elaborating the execution scenarios, collecting output metrics and storing and analyzing them online in real-time as well as later in off-line mode. Using a use-case model, the authors show that the...
Summary. Knowledge of in-situ stress distribution, especially in the vertical direction, is vital... more Summary. Knowledge of in-situ stress distribution, especially in the vertical direction, is vital to hydraulic-fracture geometry calculations. The microfracturing technique is recognized as the best method to measure in-situ stress directly. The technique, however, is typically limited to very few measurements and, at times, it is impractical to break down and measure in-situ stress in bounding nonproducing layers. Also, in layers with significant stress variation, microfracture measurements can be reflective of an average value of the variations and thus can be misleading. Alternative techniques like core measurements can suffer from depth discrepancies and lack of measurements at in-situ conditions. Sonic-logging methods can provide continuous in-situ measurements of rock mechanical properties; however, modeling stresses from mechanical properties have certain limitations. In this paper, we present a method properties have certain limitations. In this paper, we present a method th...
Hardware software cosynthesis process tries to determine system architecture for an embedded appl... more Hardware software cosynthesis process tries to determine system architecture for an embedded application. In this paper, a new cosynthesis approach is presented, which targets distributed memory architectures for high performance embedded systems. The target embedded ...
Mobile Agents (MAs) have been proposed as a solution for distributed Network Management (NM). How... more Mobile Agents (MAs) have been proposed as a solution for distributed Network Management (NM). However, most MA-based infrastructures exhibit scalability limitations when data intensive management applications are considered. Therefore, we present three novel applications, tailored to transfers of bulk network monitoring data, in which MAs are used to perform data aggregation, acquire atomic SNMP table views and support selective retrieval of SNMP table objects that meet specific selection criteria. The proposed ...
ACM Transactions on Asian and Low-Resource Language Information Processing
Social media platforms have made increasing use of irony in recent years. Users can express their... more Social media platforms have made increasing use of irony in recent years. Users can express their ironic thoughts with audio, video, and images attached to text content. When you use irony, you are making fun of a situation or trying to make a point. It can also express frustration or highlight the absurdity of a situation. The use of irony in social media is likely to continue to increase, no matter the reason. By using syntactic information in conjunction with semantic exploration, we show that attention networks can be enhanced. Using learned embedding, unsupervised learning encodes word order into a joint space. By evaluating the entropy of an example class and adding instances, the active learning method uses the shared representation as a query to retrieve semantically similar sentences from a knowledge base. In this way, the algorithm can identify the instance with the maximum uncertainty and extract the most informative example from the training set. An ironic network traine...
A serious issue in today’s society is Depression which can have a devastating impact on a person’... more A serious issue in today’s society is Depression which can have a devastating impact on a person’s ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, we decided to investigate the language used in individuals’ personal expressions to identify depressive symptoms via social media. Graph Attention Networks (GATs) are used in this study as a solution to the problems associated with text classification of depression. These GATs can be constructed using masked self-attention layers. Rather than requiring expensive matrix operations such as similarity or knowledge of network architecture, this study implicitly assigns weights to each node in a neighbourhood. This is possible because nodes and words can carry the properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the...
ACM Transactions on Asian and Low-Resource Language Information Processing
With the increasing use of online mediums, Internet-delivered psychological treatments (IDPs) are... more With the increasing use of online mediums, Internet-delivered psychological treatments (IDPs) are becoming an essential tool for improving mental disorders. Online-based health therapies can help a large segment of the population with little resource investment. The task is greatly complicated by the overlapping emotions for specific mental health. Early adoption of a deep learning system presented severe difficulties, including ethical and legal considerations that contributed to a lack of trust. Modern models required highly interpretable, intuitive explanations that humans could understand. To achieve this, we present a deep attention model based on fuzzy classification that uses the linguistic features of patient texts to build emotional lexicons. In medical applications, a diversified dataset generates work. Active learning techniques are used to extend fuzzy rules and the learned dataset gradually. From this, the model can gain a reduction in labeling efforts in mental health ...
In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatm... more In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attenti...
ACM Transactions on Asian and Low-Resource Language Information Processing, 2022
Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet int... more Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes...
Effective vector representation has been proven useful for transaction classification and cluster... more Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the resu...
High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utili... more High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insert...
Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs an... more Nowadays, embedded systems are comprised of heterogeneous multi-core architectures, i.e., CPUs and GPUs. If the application is mapped to an appropriate processing core, then these architectures provide many performance benefits to applications. Typically, programmers map sequential applications to CPU and parallel applications to GPU. The task mapping becomes challenging because of the usage of evolving and complex CPU- and GPU-based architectures. This paper presents an approach to map the OpenCL application to heterogeneous multi-core architecture by determining the application suitability and processing capability. The classification is achieved by developing a machine learning-based device suitability classifier that predicts which processor has the highest computational compatibility to run OpenCL applications. In this paper, 20 distinct features are proposed that are extracted by using the developed LLVM-based static analyzer. In order to select the best subset of features, fe...
Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, a... more Consumer demand for automobiles is changing because of the vehicle’s dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achie...
International Journal of Data Warehousing and Mining, 2016
In this paper, the authors propose an approach and different tools to evaluate the performance an... more In this paper, the authors propose an approach and different tools to evaluate the performance and assess the effectiveness of a model in the field of dynamic cubing. Experimental evaluation, on one hand allows observing the behavior and the performance of the solution, while on the other hand it lets one compare the results with those of the other competing solutions. The authors' proposal includes an experimental workflow based on a set of configuration parameters to characterize the inputs (data sets, queries sets and algorithm input parameters) and a set of metrics to analyze and qualify the output (performance and behavior metrics) of the solution. They have identified a number of useful tools necessary to develop an experimental evaluation strategy. These monitoring tools allow elaborating the execution scenarios, collecting output metrics and storing and analyzing them online in real-time as well as later in off-line mode. Using a use-case model, the authors show that the...
Summary. Knowledge of in-situ stress distribution, especially in the vertical direction, is vital... more Summary. Knowledge of in-situ stress distribution, especially in the vertical direction, is vital to hydraulic-fracture geometry calculations. The microfracturing technique is recognized as the best method to measure in-situ stress directly. The technique, however, is typically limited to very few measurements and, at times, it is impractical to break down and measure in-situ stress in bounding nonproducing layers. Also, in layers with significant stress variation, microfracture measurements can be reflective of an average value of the variations and thus can be misleading. Alternative techniques like core measurements can suffer from depth discrepancies and lack of measurements at in-situ conditions. Sonic-logging methods can provide continuous in-situ measurements of rock mechanical properties; however, modeling stresses from mechanical properties have certain limitations. In this paper, we present a method properties have certain limitations. In this paper, we present a method th...
Hardware software cosynthesis process tries to determine system architecture for an embedded appl... more Hardware software cosynthesis process tries to determine system architecture for an embedded application. In this paper, a new cosynthesis approach is presented, which targets distributed memory architectures for high performance embedded systems. The target embedded ...
Mobile Agents (MAs) have been proposed as a solution for distributed Network Management (NM). How... more Mobile Agents (MAs) have been proposed as a solution for distributed Network Management (NM). However, most MA-based infrastructures exhibit scalability limitations when data intensive management applications are considered. Therefore, we present three novel applications, tailored to transfers of bulk network monitoring data, in which MAs are used to perform data aggregation, acquire atomic SNMP table views and support selective retrieval of SNMP table objects that meet specific selection criteria. The proposed ...
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