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With the wide deployment of the Internet of Things (IoT), large volumes of incomplete multiview data that violates data integrity is generated by various applications, which inevitably produces negative impacts on the quality of service of IoT systems. Incomplete multiview clustering (IMC), as an essential technique of data processing, has the potential for mining patterns of incomplete IoT data. However, previous methods utilize notion-strong distances that can only measure differences between distributions at the overlap of data manifolds in fusing complementary information of data for pattern mining. They may suffer from biased estimation and information loss in capturing intrinsic structures of incomplete multiview data. To address these challenges, a semidiscrete multiview optimal transport (SD-MOT) is defined for IMC, which utilizes distances with weak notions to capture intrinsic structures of incomplete multiview data. Specifically, IMC is recast as an equivalent optimal transport between continuous incomplete multiview data and discrete clustering centroids, to avoid the strict assumption on overlap between manifolds in pattern mining. Then, SD-MOT is instantiated as a deep incomplete contrastive clustering network to remedy biased estimation and information loss on intrinsic structures of incomplete multiview data. Afterwards, a variational solution to SD-MOT is derived to effectively train the network parameters for pattern mining. Finally, extensive experiments on four representative incomplete multiview datasets verify the superiority of SD-MOT in comparison with nine baseline methods. [ABSTRACT FROM AUTHOR]

This article proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets, which are tweets with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of 90% when analyzing complex Wikipedia data while the other algorithms did not achieve 85% when processing the same amount of data. [ABSTRACT FROM AUTHOR]

Cyber-manufacturing Systems (CMS) have been growing in popularity, transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Therefore, it is likely that this newmanufacturing model will become increasingly popular. By building new technologies on top of existing CMS, these systems will ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim; some challenges remain to be overcome. In general, the use of CMS can revolutionize the manufacturing industry. This study comprehensively analyzes these systems and their potential applications and implications. An overview of the field is then given and various aspects of CMS are also explored with more details. A taxonomy of the most common and current approaches to CMS is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, our survey identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, we identify several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. In summary, this paper presents a comprehensive overview of current technology and valuable insights are provided for the potential impact of CMS on society and industry. [ABSTRACT FROM AUTHOR]

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N ×M ×3 to P×Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. [ABSTRACT FROM AUTHOR]

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 attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89. [ABSTRACT FROM AUTHOR]

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, this paper investigates 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 properties and sentiments of their neighbours. Another aspect of the study that contributed to the expansion of the emotion lexicon was the use of hypernyms. As a result, our method performs better when applied to data from the Reddit subreddit Depression. Our experiments show that the emotion lexicon constructed by using the Graph Attention Network ROC achieves 0.91 while remaining simple and interpretable. [ABSTRACT FROM AUTHOR]

In recent years, intensive research has been conducted to enable people to live more comfortably. Developments in the Internet of Things (IoT), big data, and artificial intelligence have taken this type of research to a new level and led to the emergence of the Internet of Behaviors (IoB), which analyzes behavioral patterns. However, current IoB technologies are not capable of handling heterogeneous data. While it is quite common to have different formats of sensor data for the same behavioral observation, the use of these different data formats can significantly help to obtain a more accurate classification of the observation. Another limitation is that existing IoB deep learning models rely on inefficient hyperparameter tuning strategies. In this paper, we present an Advanced Deep Learning framework for IoB (ADLIoB) applied to connected vehicles. Several deep learning architectures are employed in this framework: CNN, Graph CNN (GCNN), and LSTM are used to train sensor data of different formats. In addition, a branch-and-bound technique is used to intelligently select hyperparameters. To validate ADLIoB, experiments were conducted on four databases for connected vehicles. The results clearly show that ADLIoB is superior to the baseline solutions in terms of both accuracy and runtime. [ABSTRACT FROM AUTHOR]

Software-Defined Networks (SDNs), with their segregated data and control planes, has proved to be capable of managing massive amounts of data by leveraging distributed information available across the network for informed decision-making at the network controller. However, with the proliferation of next-generation, real-time Internet of Things (IoT) applications that vary greatly in terms of data frequency and volumes, data traffic classification can substantially assist SDN controllers toward efficient routing and traffic engineering decisions. Existing works on network classification are limited by their application-centric nature, thus overlooking the key criterion for real-time IoT applications, namely, Quality of Service (QoS). In this article, we focus on augmenting SDN controllers’ decision-making capacity and Underwater Sensor Networks with machine learning algorithms to achieve real-time, QoS-aware, network traffic classification. Three classifiers, namely, Feed-forward Neural Network, Naïve Bayes, and Logistics Regression have been employed with a novel Artificial Neural Network and Particle Swarm Optimization hybridization scheme by carrying firstand second-order stability analysis for performance improvement of these classifiers. In short, the proposed framework exploits optimization algorithms and semi-supervised machine learning (ML) for precise traffic classification while keeping communication overhead between controller and switches minimal. Results obtained from real-life datasets demonstrate the efficacy of our proposed scheme. [ABSTRACT FROM AUTHOR]

High-utility sequential pattern mining (HUSPM) is a hot research topic in recent decades since it combines both sequential and utility properties to reveal more information and knowledge rather than the traditional frequent itemset mining or sequential pattern mining. Several works of HUSPM have been presented but most of them are based on main memory to speed up mining performance. However, this assumption is not realistic and not suitable in large-scale environments since in real industry, the size of the collected data is very huge and it is impossible to fit the data into the main memory of a single machine. In this article, we first develop a parallel and distributed three-stage MapReduce model for mining high-utility sequential patterns based on large-scale databases. Two properties are then developed to hold the correctness and completeness of the discovered patterns in the developed framework. In addition, two data structures called sidset and utilitylinked list are utilized in the developed framework to accelerate the computation for mining the required patterns. From the results, we can observe that the designed model has good performance in large-scale datasets in terms of runtime, memory, efficiency of the number of distributed nodes, and scalability compared to the serial HUSP-Span approach. [ABSTRACT FROM AUTHOR]

Multiple robots negotiating in a dynamic workspace may lead to collisions. To avoid such issues, multi-robot navigation and coordination becomes necessary but is computationally very challenging, particularly when there are many robots. This article addresses the problem of multi-robot navigation where individual robots require coordination. Although a few such attempts for modeling multi-robot coordination and navigation have been studied, this work proposes a game-theoretic coordination strategy, also referred to as strategic coordination. We make use of a genetic algorithm tuned fuzzy logic--based motion planner. The proposed strategic coordination strategy has been pitted against a basic potential field-based motion planner, also referred to as the heuristic method, for performance comparison. Results are compared through computer simulation with 8 to 17 robots at different rounds. From the obtained results, it was observed that the proposed coordination scheme's efficacy is strong for a larger number of robots. In addition, the proposed strategic coordination scheme with the genetic-fuzzy-based motion planner was found to outperform other combinations as far as the quality of solutions and time to reach the goal positions. The computational complexity of different methods has also been compared and presented. [ABSTRACT FROM AUTHOR]

In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates. [ABSTRACT FROM AUTHOR]

The essence of "blockchain" is a shared database in which information stored is un-falsifiable, traceable, open, and transparent. Therefore, to improve the security of private information in medical systems, this article uses blockchain technology to design a method to protect private information in medical systems and effectively realize anti-theft control of private information. First, the Patient-oriented Privacy Preserving Access Control model is introduced into the access control process of private information in medical systems. Next, a private information storage platform is built by using blockchain technology, and information transmission is realized using standard cryptographic algorithms. In this process, file authorization contracts are also used to guarantee the security of private information and further prevent theft of medical private information. Our simulation results show that the storage response time of this method is kept below 1,000 ms, and the maximum information throughput rate reaches 550 kbit/s, which indicates that this method has strong performance in information storage and transmission efficiency. Moreover, the reliability and bandwidth utilization of data transmission across domains is higher, so the method has higher information security control performance and superior overall performance. [ABSTRACT FROM AUTHOR]

With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers' API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs' actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WARtext). WARtext guarantees the compatibility among the recommended APIs by utilizing the APIs' composition records produced by historicalmashup creations. Besides, WARtext entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WARtext can precisely capture the developers' functional requirements and then return a set of APIs with the highest compatibility. Finally, through a realworld mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach. [ABSTRACT FROM AUTHOR]

Recently, Content Delivery Networks (CDN) have become more and more popular. The technology itself is ahead of academic research in this area. Several dimensions of the technology have not been adequately investigated by academia. These dimensions include outline management, security, and standardization. Discovering and highlighting aspects of this technology that may have or have not been covered by academic research is the first step toward helping academia bridge the gap with industry or even go one step further to lead industry in the right direction. This suggests a comprehensive survey on research works in this regard. The literature in this area has already come up with some surveys and taxonomies, but some of them are outdated or do not cover every aspect of CDN while others fail to detect existing trends or to develop a holistic roadmap for research on the technology. Furthermore, none of the existing surveys aim at enlightening the dark aspects of the technology that have not been subject to academic research. In this survey, we first extract the lifecycle of a CDN as suggested by the existing research. Then, we investigate previous relevant works on each phase of the lifecycle to clarify where the research is currently located and headed. We show how CDN technology tends to converge with emerging paradigms such as cloud computing, edge computing, and machine learning, which are more mature in terms of academic research. This helps us determine the right direction for further research by revealing the deficiencies in existing works. [ABSTRACT FROM AUTHOR]

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through nonsub- sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/ recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them. [ABSTRACT FROM AUTHOR]