Reviewerly/exHarmony huggingface dataset:
MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation

Combining color (RGB) images with thermal images can facilitate semantic segmentation of poorly lit urban scenes. However, for RGB-thermal (RGB-T) semantic segmentation, most existing models address cross-modal feature fusion by focusing only on exploring the samples while neglecting the connections between different samples. Additionally, although the importance of boundary, binary, and semantic information is considered in the decoding process, the differences and complementarities between different morphological features are usually neglected. In this paper, we propose a novel RGB-T semantic segmentation network, called MMSMCNet, based on modal memory fusion and morphological multiscale assistance to address the aforementioned problems. For this network, in the encoding part, we used SegFormer for feature extraction of bimodal inputs. Next, our modal memory sharing module implements staged learning and memory sharing of sample information across modal multiscales. Furthermore, we constructed a decoding union unit comprising three decoding units in a layer-by-layer progression that can extract two different morphological features according to the information category and realize the complementary utilization of multiscale cross-modal fusion information. Each unit contains a contour positioning module based on detail information, a skeleton positioning module with deep features as the primary input, and a morphological complementary module for mutual reinforcement of the first two types of information and construction of semantic information. Based on this, we constructed a new supervision strategy, that is, a multi-unit-based complementary supervision strategy. Extensive experiments using two standard datasets showed that MMSMCNet outperformed related state-of-the-art methods. The code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/2021nihao/MMSMCNet</uri> .
Deep neural networks in the cloud: Review, applications, challenges and research directions

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.
An image encryption scheme based on chaotic system and compressed sensing for multiple application scenarios

In this paper, we propose a novel image encryption scheme based on a chaotic system and compressed sensing for multiple application scenarios. It is a dedicated scheme for JPEG format that makes full use of the characteristics of DCT data. The proposed scheme generates a key from a plain image with random noise, which is then used to generate the parameters needed in the scheme through using a new chaotic system structure. This structure employs data segmentation for the DCT data of a plain image and processes sparse data and non-sparse data separately: it permutes non-sparse data, and applies permutation and compressed sensing to sparse data. By merging the processed data and performing efficient confusion, we obtain the final encrypted data. The reverse operation is image decryption, and partial decryption can be performed quickly since sparse data is not required. Such partial decryption can be used for multiple applications, such as copyright protection platforms and machine learning based on private data. Simulation experiments and a performance analysis show that the key generation method and efficient confusion of our scheme can improve the security of image encryption. Furthermore, the data segmentation and reconstruction algorithm based on a cyclic matrix can greatly improve decryption efficiency.
Automatic design of machine learning via evolutionary computation: A survey

Machine learning (ML), as the most promising paradigm to discover deep knowledge from data, has been widely applied to practical applications, such as recommender systems, virtual reality, and semantic segmentation. However, building a high-quality ML system for given tasks requires expert knowledge and high computation cost. This poses a significant challenge to the further development of ML in large-scale practical applications. The automatic design of ML has become an increasingly popular research trend. At the same time, evolutionary computation (EC), as an excellent heuristic search technique, has been widely employed in ML optimization, so-called evolutionary machine learning (EML). In this paper, we offer a comprehensive review of the literature (more than 500 references) for EML methods. We first introduce the concepts related to ML and EC. After that, we propose a taxonomy criterion based on the ML and EC perspectives. The important research problems of EML, e.g., ML algorithms, solution representations, search paradigms, acceleration strategies and applications, are reviewed systematically. Lastly, we analyze EML limitations and discuss potential trends that are promising to address in the future.
Improved YOLOX-X based UAV aerial photography object detection algorithm

Unmanned Aerial Vehicle (UAV) aerial photography object detection has high research significance in the fields of disaster rescue, ecological environmental protection, and military reconnaissance. The larger width of UAV photography introduces background interference into the detection task, whereas the relatively high imaging height of the UAV results in mostly small objects in the aerial images. YOLOX-X operated fast and achieved advanced results on MS COCO of natural scene images, so YOLOX-X was used as the baseline network in this paper. A UAV aerial photography object detection algorithm YOLOX_w with improved YOLOX-X is proposed to handle the characteristics of complex backgrounds and the large number of small objects in UAV aerial photography images. The model’s performance in detecting small objects is first improved by preprocessing the training set with the slicing aided hyper inference (SAHI) algorithm and by data augmentation. Then, a shallow feature map with rich spatial information is introduced into the path aggregation network (PAN), and a detection head is added to detect small objects. Next, the ultra-lightweight subspace attention module (ULSAM) is added to the PAN stage to highlight the target features and weaken the background features, which improves the detection accuracy of the network. Finally, the loss function of the bounding box regression is optimized to further improve network prediction accuracy. Experimental results on the VisDrone dataset demonstrate that the detection accuracy of the proposed YOLOX_w algorithm improved by 8% when compared with the baseline YOLOX-X. Moreover, migration experiments on the DIOR dataset verify the effectiveness and robustness of the improved method.
The augmentation effect of artificial intelligence: can AI framing shape customer acceptance of AI-based services?

ABSTRACTABSTRACTAlthough Artificial Intelligence is a big revolution in the tourism and hospitality industry, prior research provides little insight into how customers respond to AI replacement and how providers can mitigate AI aversion. Drawing on the Feeling Economy framework, three studies examine how customers react to a different framing of AI replacement (augmentation vs. substitution) compared to using only human employees, affecting their acceptance of AI-based services. The findings contribute to the tourism and hospitality literature by revealing that framing AI as augmentation (vs. substitution) can increase enjoyment and ease of use and improve AI acceptance. Consistent with the Feeling Economy account, the findings highlight the proposed mechanism of enjoyment and perceived ease of use underlying the AI framing effects. This research provides important theoretical and managerial implications for tourism and hospitality providers, helping them understand how to effectively introduce AI-based services to win customers' acceptance.KEYWORDS: Artificial intelligencefeeling economyhospitalitytourismtechnology acceptanceaugmentation‌ Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work received partial support from national funds through FCT (Fundação para a Ciência e a Tecnologia) , under the project – UIDB/04152/2020 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS and 2021.09496.BD.
Blockchain adoption in logistics companies in Ho Chi Minh City, Vietnam

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Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey

Anomaly detection in video surveillance is a highly developed subject that is attracting increased attention from the research community. There is great demand for intelligent systems with the capacity to automatically detect anomalous events in streaming videos. Due to this, a wide variety of approaches have been proposed to build an effective model that would ensure public security. There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. Deep learning has been successfully applied to many aspects of computer vision. In particular, the strong growth of generative models means that these are the main techniques used in the proposed methods. This paper aims to provide a comprehensive review of the deep learning-based techniques used in the field of video anomaly detection. Specifically, deep learning-based approaches have been categorized into different methods by their objectives and learning metrics. Additionally, preprocessing and feature engineering techniques are discussed thoroughly for the vision-based domain. This paper also describes the benchmark databases used in training and detecting abnormal human behavior. Finally, the common challenges in video surveillance are discussed, to offer some possible solutions and directions for future research.
Networks Data Transfer Classification Based On Neural Networks

Data transmission classification is an important issue in networks communications, since the data classification process has the ultimate impact in organizing and arranging it according to size and area to prepare it for transmission to minimize the transmission bandwidth and enhancing the bit rate. There are several methods and mechanisms for classifying the transmitted data according to the type of data and to the classification efficiency. One of the most recent classification methods is the classification of artificial neural networks (ANN). It is considered one of the most dynamic and up-to-date research in areas of application. ANN is a branch of artificial intelligence (AI). The neural network is trained by backpropagation algorithm. Various combinations of functions and their effect while utilizing ANN as a file, classifier was studied and the validity of these functions for different types of datasets was analyzed. Back propagation neural university (BPNN) supported with Levenberg Marqurdte (LM) activation function might be utilized with as a successful data classification tool with a suitable set of training and learning functions which operates, when the probability is maximum. Whenever the maximum likelihood method was compared with backpropagation neural network method, the BPNN supported with Levenberg Marqurdte (LM) activation function was further accurate than maximum likelihood method. A high predictive ability against stable and well-functioning BPNN is possible. Multilayer feed-forward neural network algorithm is also used for classification. However BPNN supported with Levenberg Marqurdte (LM) activation function proves to be more effective than other classification algorithms.
Examining dimensions of teachers’ digital competence: A systematic review pre- and during COVID-19

The digitisation of education has heightened the importance of examining which competences are needed among teachers and student teachers. In the past decade, the opportunities and challenges related to using digital technologies in teaching and training have made the concept of 'digital competence' increasingly relevant. This paper examines how researchers have characterised the dimensions of teachers' digital competences both before and during the COVID-19 pandemic. In a literature review, 116 articles were analysed to identify prevalent understandings of teachers' and student teachers' digital competence. The search was conducted in two rounds: the period up to and including 2019 and supplements from 2020 to 2021. The latter search focused on literature addressing school closures because of 'lockdowns'. The findings indicate that research on teachers' digital competence seems unclear regarding who benefits from teachers' digital competence, the teacher's role and the links between competence and school subject domains. Moreover, teachers have a more functional role than a designer role. In addition, studies on digital competence are typically based on self-reported data, and most publications that have examined the concept of digital competence include knowledge, skills or attitudes. The COVID-19 pandemic seems to have increased the focus on the whole group of pupils and on the use of ready-made educational designs. The pandemic may also have increased researchers' reliance on self-reported data.
A survey on Named Entity Recognition — datasets, tools, and methodologies

Natural language processing (NLP) is crucial in the current processing of data because it takes into account many sources, formats, and purposes of data as well as information from various sectors of our economy, government, and private and public lives. We perform a variety of NLP operations on the text in order to complete certain tasks. One of them is NER (Named Entity Recognition). An act of recognizing and categorizing named entities that are presented in a text document is known as named entity recognition. The purpose of NER is to find references of rigid designators in the text which belong to established semantic kinds like a person, place, organization, etc. It acts as a cornerstone for many Information Extraction-related activities. In this work, we present a thorough analysis of several methodologies for NER ranging from unsupervised learning, rule-based, supervised learning, and various Deep Learning based approaches. We examine the most relevant datasets, tools, and deep learning approaches like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short Term Memory, Transfer learning approaches, and numerous other approaches currently being used in present-day NER problem environments and their applications. Finally, we outline the difficulties NER systems encounter and future directions.
Design of Nonlinear Marine Predator Heuristics for Hammerstein Autoregressive Exogenous System Identification with Key-Term Separation

Swarm-based metaheuristics have shown significant progress in solving different complex optimization problems, including the parameter identification of linear, as well as nonlinear, systems. Nonlinear systems are inherently stiff and difficult to optimize and, thus, require special attention to effectively estimate their parameters. This study investigates the parameter identification of an input nonlinear autoregressive exogenous (IN-ARX) model through swarm intelligence knacks of the nonlinear marine predators’ algorithm (NMPA). A detailed comparative analysis of the NMPA with other recently introduced metaheuristics, such as Aquila optimizer, prairie dog optimization, reptile search algorithm, sine cosine algorithm, and whale optimization algorithm, established the superiority of the proposed scheme in terms of accurate, robust, and convergent performances for different noise and generation variations. The statistics generated through multiple autonomous executions represent box and whisker plots, along with the Wilcoxon rank-sum test, further confirming the reliability and stability of the NMPA for parameter estimation of IN-ARX systems.
Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
Blockchain Applications in Sustainable Smart Cities

Sustainable smart cities aim to optimize projected complexities, costs, and environmental challenges accompanied by growing urbanization. The fundamental objectives of sustainable smart cities are to ingeniously utilize available resources and knowledge systems to provide excellent services and the finest quality of life to its citizens. The intelligent use of Information and Communication Technologies (ICT) plays a vital role in realizing smart cities. The purpose of this work is to investigate the role of blockchain technology (BCT) in sustainable smart city applications by providing a detailed study of blockchain applications in the food supply chain, tourism industry, smart healthcare system, intelligent transportation system, next-generation 5G and 6G communication, and smart energy management. Moreover, a statistical study regarding selected articles shows the significance of the underlying study. Finally, a summary of recent trends, challenges, and future research directions is presented for the BCT role in sustainable smart cities.
Making Cities Smarter

The internet of things (IoT) entails all devices that can get onto the internet. This is mainly because of the technological advancement. This exponential growth of IoT increases on the dense nodes with a huge data volume on the network that affect the collision and network congestion probabilities. This chapter presents a comprehensive description of the central and supporting innovations that are used to make cities smarter, focusing on the fifth generation (5G) IoT paradigm from a software-based network viewpoint. Furthermore, the main initiatives of international significance are discussed. Also, the chapter presents software-defined networking (SDN), IoT, and network function virtualization (NFV) challenges as it relates to the user privacy and security, IoT security, energy consumption, integration of IoT with subsystems, and architecture design. A segment of the top five future trends that are made and will make cities smarter is conclusively included.
Framework of Air Pollution Assessment in Smart Cities using IoT with Machine Learning Approach

Exhale and inhale filthy air has major health consequences. Air pollution's influence may be mitigated by conducting regular monitoring and keeping a record of it. Government organizations may also take proactive measures to protect the environment by accurately anticipating pollution levels in real time. In future smart cities, we propose using the Internet of Things and machine learning to track pollution levels in the air we breathe. The Pearson correlation test is performed to see whether pollutants and meteorological indicators have a high link. A cloud-centric IoT middleware architecture is used in this research instead of a standard sensor network to gather data from both air pollution and current weather sensors. This means that both reliability and cost have been greatly improved. Sulphur Dioxide (SO2) and Particulate Matter levels were predicted using an Artificial Neural Network (ANN) (PM2.5). The positive results show that ANNs may be used to monitor and forecast air pollution. RMSE values of 0.0128 and 0.0001 for SO2 and PM2.5 were found using our models.
Symmetrized Liouvillian Gap in Markovian Open Quantum Systems

Markovian open quantum systems display complicated relaxation dynamics. The spectral gap of the Liouvillian characterizes the asymptotic decay rate toward the steady state, but it does not necessarily give a correct estimate of the relaxation time because the crossover time to the asymptotic regime may be too long. We here give a rigorous upper bound on the transient decay of autocorrelation functions in the steady state by introducing the symmetrized Liouvillian gap. The standard Liouvillian gap and the symmetrized one are identical in an equilibrium situation but differ from each other in the absence of the detailed balance condition. It is numerically shown that the symmetrized Liouvillian gap always gives a correct upper bound on the decay of the autocorrelation function, but the standard Liouvillian gap does not.
Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.