Social media especially Twitter has become ubiquitous among people where they express their opinions on various domains. This paper presents a Hybrid Firefly – Ontology-based Clustering (FF-OC)... Show More

Social media especially Twitter has become ubiquitous among people where they express their opinions on various domains. This paper presents a Hybrid Firefly – Ontology-based Clustering (FF-OC) algorithm which attempts to extract factors impacting a major public issue that is trending. In this research work, the issue of food price rise and disease which was trending during the time of the investigation is considered. The novelty of the algorithm lies in the fact that it clusters the association rules without any prior knowledge. The findings from the experimentation suggest different factors impacting the rise of price in food items and diseases such as diabetes, flu, zika virus. The empirical results show the significant improvement when compared with Artificial Bees Colony, Cuckoo Search Algorithm, Particle Swarm Optimization, and Ant Colony Optimization based clustering algorithms. The proposed method gives an improvement of 81% in terms of DB index, 79% in terms of silhouette index, 85% in terms of C index when compared to other algorithms.

This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus... Show More

This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus fertilization ontology, which covers knowledge about citrus fertilizers and fertilization application. Specially, our approach can provide not only the yearly fertilization quantities of required pure nitrogen, phosphorus, and potassium according to their disease symptoms, but also the suitable fertilizing recommendations for the citrus orchards with different soil properties. The current version of the ontology (ver. 2.9.10) contains 103 classes, 34 properties, 800 instances, which are defined by 3056 RDF triples and is evaluated by using 90 competency questions. Furthermore, we run experiments with our proposal targeting at four citrus orchards in Chongqing, and compare its outputs with the reference values advised by the agri-professionals of citrus planting.

Under the heavy management on the increasing 3D models, the topic of image-based 3D model retrieval which organizes unlabeled 3D models based on abundant knowledge learned from labeled 2D images has... Show More

Under the heavy management on the increasing 3D models, the topic of image-based 3D model retrieval which organizes unlabeled 3D models based on abundant knowledge learned from labeled 2D images has drawn attention. However, prior methods are limited in aligning semantically at corresponding categories of two domains due to the lack of label information in the 3D domain. To this end, this paper proposes an improved semantic representation learning by multiple clustering approach, which improves the reliability of pseudo labels for 3D models, so as to achieve class-level semantic alignment. Specifically, this paper first extracts features for 2D images and 3D models. Then it clusters combining the 3D features with the semantic information from multiple clustering on 3D model features to obtain more reliable target pseudo labels. Extensive experiments have shown that the proposed method has achieved the gain of 3.0%-205.0% averagely for popular retrieval metrics on the benchmark of monocular image-based 3D object retrieval (MI3DOR), and 1.3%-69.7% on another advanced benchmark, MI3DOR-2.

This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the... Show More

This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.

The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to interpret for a non-expert user. Here, the use of natural language... Show More

The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to interpret for a non-expert user. Here, the use of natural language generation (NLG)-based techniques can help to represent explanations in a human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to the unavailability of a relevant annotated dataset for development and testing, the authors also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.

This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The main objective is to investigate the ability of virtual... Show More

This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The main objective is to investigate the ability of virtual reality to enhance the training. An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is twofold. One side investigates the new requirements for the enhancement of neurosurgeon training and adoption on a virtual reality simulator. The other side contributes to the body of knowledge related to the required ergonomics skills.

This paper introduces an approach for the VM migration based on optimization algorithm, named CS in cloud. The provider to be selected is carried out with the usage of multiple constraints, like... Show More

This paper introduces an approach for the VM migration based on optimization algorithm, named CS in cloud. The provider to be selected is carried out with the usage of multiple constraints, like delay, bandwidth, cost, and load. Subsequently, the effective searching criteria are computed for finding the optimal service on the basis of fitness constraints. The searching criteria are formulated as optimization problems, which are tackled using CS. The proposed CS is designed by integrating CSO with the SSA such that the fitness function is evaluated for the optimal VM migration by considering several parameters, such as delay, cost, bandwidth, and load. Thus, the cloud manager will perform the migration of VM in cloud based on proposed CS-based VM migration approach. The performance of the CS-based VM migration is evaluated in terms of delay, cost, and load. The proposed CS-based VM migration method achieves the minimal delay of 0.146, minimal cost of 0.052, and the minimal load of 0.182.

The research on underwater wireless sensor networks (UWSNs) has grown considerably in recent years where the main focus remains to develop a reliable communication protocol to overcome its... Show More

The research on underwater wireless sensor networks (UWSNs) has grown considerably in recent years where the main focus remains to develop a reliable communication protocol to overcome its challenges between various underwater sensing devices. The main purpose of UWSNs is to provide a low cost and an unmanned data collection system for a range of applications such as offshore exploration, pollution monitoring, oil and gas pipeline monitoring, surveillance, etc. One of the common types of UWSNs is linear sensor network (LSN), which speciall targets monitoring the underwater oil and gas pipelines. Under this application, in most of the previously proposed works, networks are deployed without considering the heterogeneity and capacity of the various sensor nodes. This negligence leads to the problem of inefficient data delivery from the sensor nodes deployed on the pipeline to the surface sinks. In addition, the existing path planning algorithms do not consider the network coverage of heterogeneous sensor nodes.

Obesity is one of the most pressing issues in society today. Virtual reality has been used in the design of tools that promotes obesity control. However, the design of current VR tools lacks the... Show More

Obesity is one of the most pressing issues in society today. Virtual reality has been used in the design of tools that promotes obesity control. However, the design of current VR tools lacks the involvement of prospective users and health practitioners. Such engagement is crucial in gathering semantic information that identifies stakeholders’ needs and ensures that all aspects of health are considered. Therefore, this paper aims to study the sociodemographic factors and individual-level characteristics and preferences that make the design of any obesity-control VR tool effective and satisfactory for a wide range of users. The paper also aims to solicit opinions of health practitioners to identify best health aspects that should be available in the design of any VR tool for obesity control. Organizations, businesses, and people will be able to readily augment such VR technologies on the semantic web, as well as on personal and mobile devices.

Consent is one of GDPR’s lawful bases for data processing and specific requirements for it apply. Consent should be specific, unambiguous and most of all informed. However, an informed consent... Show More

Consent is one of GDPR’s lawful bases for data processing and specific requirements for it apply. Consent should be specific, unambiguous and most of all informed. However, an informed consent request does not guarantee having individuals who are aware of what it means to consent and the implications that follow. Consent is often given blindly now, in particular because of information overload from long privacy policies written in legal language and complex interface designs that cause consent fatigue on the users' side. This paper presents a knowledge graph-based user interface for consent solicitation, which uses gamification to raise the legal awareness and ease individual’s comprehension of consent. The knowledge graph models informed consent in a machine-readable format and provides a unified consent model to all entities involved in the data sharing process. The evaluation shows that with the help of gamification, the interface can raise individuals' average legal awareness to 92.86%.

This paper solves the internet of things (IoT) security issues by introducing a chaotic whale crow (CWC) optimization, which is the integration of chaotic whale optimization algorithm (CWOA) in crow... Show More

This paper solves the internet of things (IoT) security issues by introducing a chaotic whale crow (CWC) optimization, which is the integration of chaotic whale optimization algorithm (CWOA) in crow search algorithm (CSA). The framework operates on two crucial aspects: one is to select the secure nodes, and the other is to implement secure routing using the selected trusted nodes. First, the selection of trusted nodes is performed based on trust factors like direct, indirect, forwarding rate, integrity, and availability factors. Then, the selected trusted nodes are adapted for trust-based secure routing, which is optimally performed using the proposed CWC, based on the fitness parameters trust and energy. Finally, the proposed CWC is evaluated, which revealed high performance with a minimal delay of 191.46ms, which shows 14.87%, 7.35%, 6.82%, 4.19%, and 5.74% improved performance compared to existing LaSeR, PM Ipv6, secTrust-RPL RISA, and LSDAR techniques. Similarly, the proposed method obtained the maximal energy of 71.25J and maximal throughput of 129.77kbps.

An increasing number of ontologies demand the interoperability between them in order to gain accurate information. The ontology heterogeneity also makes the interoperability process even more... Show More

An increasing number of ontologies demand the interoperability between them in order to gain accurate information. The ontology heterogeneity also makes the interoperability process even more difficult. The existing ontology matching systems are mainly focusing on subject derivatives of the concern domain. Since ontologies are represented as data models in a structured format, in this paper, a new modified model of similarity spreading for ontology mapping is proposed. In this approach, the mapping mainly involves with node clustering based on edge affinity, and then the graph matching is achieved by applying coefficient similarity propagation. This process is carried out by iterative manner, and at the end, the similarity score is calculated for iteration. This model is evaluated in terms of precision, recall, and f-measure parameters, and it is found that it outperforms similar systems.

The application of semantic web technologies such as semantic inference to the field of the internet of things (IoT) can realize data semantic information enhancement and semantic knowledge... Show More

The application of semantic web technologies such as semantic inference to the field of the internet of things (IoT) can realize data semantic information enhancement and semantic knowledge discovery, which plays a key role in enhancing data value and application intelligence. However, mainstream semantic inference engines cannot be applied to IoT computing devices with limited storage resources and weak computing power and cannot reason about uncertain knowledge. To solve this problem, the authors propose a lightweight semantic inference engine, Tiny-UKSIE, based on the RETE algorithm. The genetic algorithm (GA) is adopted to optimize the Alpha network sequence, and the inference time can be reduced by 8.73% before and after optimization. Moreover, a four-tuple knowledge representation method with probability factors is proposed, and probabilistic inference rules are constructed to enable the inference engine to infer uncertain knowledge. Compared with mainstream inference engines, storage resource usage is reduced by up to 97.37%, and inference time is reduced by up to 24.55%.

These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation... Show More

These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social networks have become an important part of our lives, the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The numerical simulation has been performed to validate the theoretical results. Data available on Twitter related to COVID-19 vaccination is used to perform the experiment. Finally, the authors discuss the control strategy to minimize the misinformation and disinformation related to vaccination.

Educators have been calling for reform for a decade. Recent technical breakthroughs have led to various improvements in the semantic web-based education system. After last year's COVID-19 outbreak... Show More

Educators have been calling for reform for a decade. Recent technical breakthroughs have led to various improvements in the semantic web-based education system. After last year's COVID-19 outbreak, development quickened. Many countries and educational systems now concentrate on providing students with online education, which differs greatly from traditional classroom education. Online education allows students to learn at their own pace and the system. As a consequence, we may say that education has become more dynamic. In the educational system, this changing nature makes user demands difficult to identify. Many instructors suggest using machine learning, artificial intelligence, or ontology to improve traditional teaching methods. Due to the lack of survey studies examining and comparing all of the researcher's semantic web-based teaching methodologies, we decided to conduct this survey. This paper's goal is to analyse all available possibilities for semantic web-based education systems that enable new researchers to develop their knowledge.

We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our... Show More

We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.

The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating... Show More

The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization. The approach of the research is based on the rough set theory RST. RST is a great method for making a large difference in qualitative analysis situations. It's a technique to find knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services provide for cloud users and efficient service improvement for cloud providers. The simulation of the work is finished at intervals with the merchandise utilized for the formation of the philosophy framework. The simulation shows the IoT services provided by the IoT service supplier to the user are the best utilization with the parameters and ontology technique.

Companies can gain critical real-time insights into customer requirements and service evaluation by mining social media. To acquire the service performance and improve the service deficiencies for... Show More

Companies can gain critical real-time insights into customer requirements and service evaluation by mining social media. To acquire the service performance and improve the service deficiencies for hotels, this research proposes a benchmark-based performance evaluation model for hotel service to enable hotel managers to assess the service performance. In the case of non-benchmark service hotels, the identification and improvement model for non-benchmark criteria can recognize and analyze the required quantities of performance improvements for non-benchmark criteria. For understanding the causes of service deficiencies, this research mines the online posts and creates a hierarchical ontology of service deficiencies for hotels. A hierarchical ontology-based neural network is proposed to automatically identify the causes of service deficiencies. This study employs an online forum as a case to achieve the identification accuracy of causes of service deficiencies of 92.68%. The analytical result can demonstrate the significant effectiveness and practical value of the proposed methodology.

The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this... Show More

The development of citizenship competences plays an important role in a complex system like society. Thus, to analyze how such competences impact other contexts is a great challenge because this kind of study involves the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon the Semantic Web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact conflict resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences, and the number of conflicts solved is higher when using citizenship competences.

The refueling trajectory of self-driving tourists is sparse, and it is difficult to restore the real travel route. A sparse trajectory clustering algorithm is proposed based on semantic... Show More

The refueling trajectory of self-driving tourists is sparse, and it is difficult to restore the real travel route. A sparse trajectory clustering algorithm is proposed based on semantic representation to mine popular self-driving travel routes. Different from the traditional trajectory clustering algorithm based on trajectory point matching, the semantic relationship between different trajectory points is researched in this algorithm, and the low-dimensional vector representation of the trajectory is learned. First, the neural network language model is used to learn the distributed vector representation of the fueling station; then, the average of all the station vectors in each trajectory is taken as the vector representation of the trajectory. Finally, the classic k-means algorithm is used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm effectively mines two popular self-driving travel routes.

This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm... Show More

This paper presents a detection algorithm using normalized mutual information feature selection and cooperative evolution of multiple operators based on adaptive parallel quantum genetic algorithm (NMIFS MOP- AQGA). The proposed algorithm is to address the problems that the intrusion detection system (IDS) has lower the detection speed, less adaptability and lower detection accuracy. In order to achieve an effective reduction for high-dimensional feature data, the NMIFS method is used to select the best feature combination. The best features are sent to the MOP- AQGA classifier for learning and training, and the intrusion detectors are obtained. The data are fed into the detection algorithm to ultimately generate accurate detection results. The experimental results on real abnormal data demonstrate that the NMIFS MOP- AQGA method has higher detection accuracy, lower false negative rate and higher adaptive performance than the existing detection methods, especially for small samples sets.

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug... Show More

Existing pharmaceutical information extraction research often focus on standalone entity or relationship identification tasks over drug instructions. There is a lack of a holistic solution for drug knowledge extraction. Moreover, current methods perform poorly in extracting fine-grained interaction relations from drug instructions. To solve these problems, this paper proposes an information extraction framework for drug instructions. The framework proposes deep learning models with fine-tuned pre-training models for entity recognition and relation extraction. In addition, it incorporates an novel entity pair calibration process to promote the performance for fine-grained relation extraction. The framework experiments on more than 60k Chinese drug description sentences from 4000 drug instructions. Empirical results show that the framework can successfully identify drug related entities (F1 3 0.95) and their relations (F1 3 0.83) from the realistic dataset, and the entity pair calibration plays an important role (~5% F1 score improvement) in extracting fine-grained relations.

The intrusion detection system (IDS) has lower speed, less adaptability and lower detection accuracy especially for small samples sets. This paper presents a detection model based on normalized... Show More

The intrusion detection system (IDS) has lower speed, less adaptability and lower detection accuracy especially for small samples sets. This paper presents a detection model based on normalized mutual antibodies information feature selection and adaptive quantum artificial immune with cooperative evolution of multiple operators (NMAIFS MOP-AQAI). First, for a high intrusion speed, the NMAIFS is used to achieve an effective reduction for high-dimensional features. Then, the best feature vectors are sent to the MOP-AQAI classifier, in which, vaccination strategy, the quantum computing, and cooperative evolution of multiple operators are adopted to generate excellent detectors. Lastly, the data is fed into NMAIFS MOP-AQAI and ultimately generates accurate detection results. The experimental results on real abnormal data demonstrate that the NMAIFS MOP-AQAI has higher detection accuracy, lower false negative rate and a higher adaptive performance than the existing anomaly detection methods, especially for small samples sets.

Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph... Show More

Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph universal adversarial attack is a special type of attack on graph which can attack any targeted victim by flipping edges connected to anchor nodes. In this paper, we propose the forward-derivative-based graph universal adversarial attack (FDGUA). Firstly, we point out that one node as training data is sufficient to generate an effective continuous attack vector. Then we discretize the continuous attack vector based on forward derivative. FDGUA can achieve impressive attack performance that three anchor nodes can result in attack success rate higher than 80% for the dataset Cora. Moreover, we propose the first graph universal adversarial training (GUAT) to defend against universal adversarial attack. Experiments show that GUAT can effectively improve the robustness of the GNNs without degrading the accuracy of the model.

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge... Show More

The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provides access to commonsense knowledge about household environments and performs semantic matching between entities from the web knowledge graph ConceptNet, using semantic knowledge from DBpedia and WordNet, with the ones existing in the knowledge graph. They offer a set of predefined SPARQL templates that directly address the ontology on which their knowledge retrieval framework is built and querying through SPARQL. The framework also features an argumentation component, where the user can argue against the answers of the knowledge retrieval component of the framework under two different scenarios: the missing knowledge scenario, where an entity should be in the answers, and the wrong knowledge scenario, where an entity should not be in the answers. This argumentation dialogue can end up in learning a new piece of knowledge when the user wins the dialogue.

The aim of this work is the development of an information system that, by integrating data from different sources and applying semantic technologies, makes it possible to publish and share with... Show More

The aim of this work is the development of an information system that, by integrating data from different sources and applying semantic technologies, makes it possible to publish and share with society the scientific production generated in the university environment, promoting its dissemination and thus contributing to the knowledge society, among others. In practice, this is the implementation of a CRIS (current research information system). This CRIS presents advanced features. On one hand it applies semantic technologies, providing a query service through a SPARQL Point, besides the reuse of shared data by exporting them in different formats. In this sense, it is also based on a European ontology or semantic standard such as CERIF, which facilitates its portability. On the other hand, CRIS also presents an alternative to the lack of a single data system by allowing data from different sources to be integrated and managed.

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They... Show More

In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.

In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims... Show More

In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims, which has aroused general concern all over the world, becoming global issues. One of the important features in human being traffic is the factor of globalization. A destination-source model works as a deterrent which is applied in the identification of smuggling and trafficking of illegal immigrants. The related results show that the employer penalty and market wage will influence the amount of smuggling and trafficking immigrants. Tax offered by legal unskilled workers at destination countries provides financial support for the inland monitoring of illegal immigrants. The improved SVM (supported vector machine) is proposed to study online textual data used for advertisement classification, with the purpose of discerning underlying human trafficking patterns on the network and recognizing suspicious advertisements, a concern of law-enforcement agencies.

In this paper the spintronic-based memory MRAM is presented that showed how it can replace both SRAM and DRAM and provide the high speed with great chip size. Moreover, MRAM is the nonvolatile... Show More

In this paper the spintronic-based memory MRAM is presented that showed how it can replace both SRAM and DRAM and provide the high speed with great chip size. Moreover, MRAM is the nonvolatile memory that provides great advancement in the storage process. The different types of MRAM are mentioned with the techniques used for writing purpose and also mention which one is more used and why. The basic working principle and the function performed by the MRAM are discussed. Artificial intelligence (AI) is mentioned with its pros and cons for intelligent systems. Neuromorphic computing is also explained along with its important role in intelligent systems. Some reasons are also discussed as to why neuromorphic computing is so important. This paper also presents how spintronic-based devices especially memory can be used in intelligent systems and neuromorphic computing. Nanoscale spintronic-based MRAM plays a key role in intelligent systems and neuromorphic computing applications.

Recently, local gradient microstructure of image textures has become an important field of texture classification, but it is generally to investigate the multiscale local microstructures of image... Show More

Recently, local gradient microstructure of image textures has become an important field of texture classification, but it is generally to investigate the multiscale local microstructures of image gradient, and rarely consider the multidirectional and multiscale local microstructure of image gradient. The proposed algorithm first extracts the two-order gradient feature of the image from different orthogonal directions and further constructs the multiple shape index of the image, and then calculates the histogram feature vectors of the shape index on different orthogonal directions and scales, and finally connects all histogram feature vectors on different orthogonal directions and scales to obtain the final matching feature vector of the image. To further enhance the discriminant ability of feature vector generated by multidirectional shape index schemes, the weight of each block of images is also considered. Experiments on two texture databases and one palmprint database have fully confirmed the effective of proposed algorithm.

Using four types of publicly available datasets and ArcGIS software, the authors identify the spatial characteristics of postgraduate education in China at three scales: comprehensive economic zone... Show More

Using four types of publicly available datasets and ArcGIS software, the authors identify the spatial characteristics of postgraduate education in China at three scales: comprehensive economic zone, provincial, and city. They also employ geographically weighted regression and ordinary least squares to study the factors influencing the spatial pattern of postgraduate education in Gin at the city scale. The findings show that the number of postgraduate education institutions increases as the longitude of a city increases, but the number decreases from coast to inland. Second, postgraduate education institutions tend to group together in provincial capitals and megacities. Finally, GDP, per capita GDP, population size, local income, and total retail sales of consumer goods significantly impact postgraduate education development. The study contributes to the literature and provides insights for practitioners in promoting urban planning and infrastructure development.

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a... Show More

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

Brain magnetic resonance images (MRI) are widely used for the classification of Alzheimer's disease (AD). The size of 3D images is, however, too large. Some of the sliced image features are lost... Show More

Brain magnetic resonance images (MRI) are widely used for the classification of Alzheimer's disease (AD). The size of 3D images is, however, too large. Some of the sliced image features are lost, which results in conflicting network size and classification performance. This article uses key components in the transformer model to propose a new lightweight method, ensuring the lightness of the network and achieving highly accurate classification. First, the transformer model is imitated by using image patch input to enhance feature perception. Second, the Gaussian error linear unit (GELU), commonly used in transformer models, is used to enhance the generalization ability of the network. Finally, the network uses MRI slices as learning data. The depthwise separable convolution makes the network more lightweight. Experiments are carried out on the ADNI public database. The accuracy rate of AD vs. normal control (NC) experiments reaches 98.54%. The amount of network parameters is 1.3% of existing similar networks.

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a... Show More

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a method to add local adversarial training to the transfer learning model and integrate the attention mechanism. The model uses ALBERT for migration pre-training and adds perturbation factors to the output matrix of the embedding layer to constitute local adversarial training. BILSTM is used to encode the shared and private features of the task, and the attention mechanism is introduced to capture the characters that focus more on the entities. Finally, the best entity annotation is obtained by CRF. Experiments are conducted on People's Daily 2004 and Tsinghua University open-source text classification datasets. The experimental results show that compared with the SOTA model, the F1 values of the proposed method in this paper are improved by 7.32 and 7.98 in the two different datasets, respectively, proving that the accuracy of the method in this paper is improved in the Chinese domain.

Due to the high similarity of fine-grained image subclasses, small inter-class changes and large intra-class changes are caused, which leads to the difficulty of fine-grained image classification... Show More

Due to the high similarity of fine-grained image subclasses, small inter-class changes and large intra-class changes are caused, which leads to the difficulty of fine-grained image classification task. However, existing convolutional neural networks have been unable to effectively solve this problem. Aiming at the above-mentioned fine-grained image classification problem, this paper proposes a multi-scale and multi-level ViT model. First, through data augmentation techniques, the accuracy of fine-grained image classification can be effectively improved. Secondly, the small-scale input and large-scale input of the model make the input image have more feature ex-pressions. The subsequent multi-layeredness effectively utilizes the results of the previous layer of ViT, so that the data of the previous layer can be more effectively used in the next layer of ViT. Finally, cross-attention allows the results of two scale inputs to be fused in a reasonable way. The proposed model is competitive with current mainstream state-of-the-art methods on multiple datasets.

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the... Show More

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the Top-k candidate frequent location item sets, and then the spatio-temporal sequence intersection and the divide and conquer merge methods are used to convert the frequent iterative calculation of long itemsets into hierarchical sets' regular operations, the superset and subset of frequent sequences are found. This kind of semantic trajectory frequent pattern mining can actively identify and discover potential carpooling needs, and provide higher accuracy for location-based intelligent recommendations such as carpooling and HOV lane travel (High-Occupancy Vehicle Lane). Carpool matching and recommendation based on semantic trajectory in this paper is suitable for single carpooling and relay-ride carpooling. the results of simulation carpooling experiments prove the applicability and efficiency of the method.

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working... Show More

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

Among the large number of network attack alerts generated every day, actual security incidents are usually overwhelmed by a large number of redundant alerts. Therefore, how to remove these redundant... Show More

Among the large number of network attack alerts generated every day, actual security incidents are usually overwhelmed by a large number of redundant alerts. Therefore, how to remove these redundant alerts in real time and improve the quality of alerts is an urgent problem to be solved in large-scale network security protection. This paper uses the method of combining machine learning and deep learning to improve the effect of false alarm detection and then more accurately identify real alarms, that is, in the process of training the model, the features of a hidden layer output of the DNN model are used as input to train the machine learning model. In order to verify the proposed method, we use the marked alert data to do classification experiments, and finally use the accuracy recall rate, precision, and F1 value to evaluate the model. Good results have been obtained.

This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector... Show More

This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of... Show More

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and chatrooms are common tools of conversations grouping. Crowdsourcing involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We analyzed online forum posts from various geographical regions in the US and characterized the readability scores of users. Specifically, we collected 10,000 tweets from the members of US Senate and computed the Flesch-Kincaid readability score. Comparing the Senators’ tweets to the ones from average internet users, we note 1) US Senators’ readability based on their tweets rate is much higher, and 2) immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

Due to the availability of cheap 3D sensors such as Kinect and LiDAR, the use of 3D data in various domains such as manufacturing, healthcare, and retail to achieve operational safety, improved... Show More

Due to the availability of cheap 3D sensors such as Kinect and LiDAR, the use of 3D data in various domains such as manufacturing, healthcare, and retail to achieve operational safety, improved outcomes, and enhanced customer experience has gained momentum in recent years. In many of these domains, object recognition is being performed using 3D data against the difficulties posed by illumination, pose variation, scaling, etc present in 2D data. In this work, we propose three data augmentation techniques for 3D data in point cloud representation that use sub-sampling. We then verify that the 3D samples created through data augmentation carry the same information by comparing the Iterative Closest Point Registration Error within the sub-samples, between the sub-samples and their parent sample, between the sub-samples with different parents and the same subject, and finally, between the sub-samples of different subjects. We also verify that the augmented sub-samples have the same characteristics and features as those of the original 3D point cloud by applying the Central Limit Theorem.

Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general... Show More

Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%.

Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge... Show More

Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.

Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory... Show More

Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task.

The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One... Show More

The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.

The research proposes an innovated structure of the density map-based crowd counting network augmented by information entropy. The network comprises of a front-end network to extract features and a... Show More

The research proposes an innovated structure of the density map-based crowd counting network augmented by information entropy. The network comprises of a front-end network to extract features and a back-end network to generate density maps. In order to validate the assumption that the entropy can boost the accuracy of density map generation, a multi-scale entropy map extraction process is imported into the front-end network along with a fine-tuned convolutional feature extraction process, In the back-end network, extracted features are decoded into the density map with a multi-column dilated convolution network. Finally, the decoded density map can be mapped as the estimated counting number. Experimental results indicate that the devised network is capable of accurately estimating the count in extremely high crowd density. Compared to similar structured networks which don’t adapt entropy feature, the proposed network exhibits higher performance. This result proves the feature of information entropy is capable of enhancing the efficiency of density map-based crowd counting approaches.

Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals... Show More

Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of... Show More

In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.

The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the... Show More

The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.

Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information... Show More

Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.

With the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online.... Show More

With the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online. Humans cannot differentiate fake news from real news because they can be easily influenced. A lot of research work has been conducted for detecting fake news using Artificial Intelligence and Machine Learning. A large number of deep learning models and their architectural variants have been investigated and many websites are utilizing these models directly or indirectly to detect fake news. However, state-of-the-arts demonstrate the limited accuracy in distinguishing fake news from the original news. We propose a multi-channel deep learning model namely Mc-DNN, leveraging and processing the news headlines and news articles along different channels for differentiating fake or real news. We achieve the highest accuracy of 99.23% on ISOT Fake News Dataset and 94.68% on Fake News Data for Mc-DNN. Thus, we highly recommend the use of Mc-DNN for fake news detection.

Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends... Show More

Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource... Show More

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.