Reviewerly/exHarmony huggingface dataset:
Toward resilient cloud warehousing via a blockchain-enabled auction approach

Cloud warehousing service (CWS) has emerged as a promising third-party logistics service paradigm driven by the widespread use of e-commerce. The current CWS billing method is typically based on a fixed rate in a coarse-grained manner. This method cannot reflect the true service value under the fluctuating e-commerce logistics demand and is not conducive to CWS resilience management. Accordingly, a floating mechanism can be considered to introduce more flexible billing. A CWS provider lacks sufficient credibility to implement floating mechanisms because it has vested interests in terms of fictitious demand. To address this concern, this report proposes a blockchain-enabled floating billing management system as an overall solution for CWS providers to enhance the security, credibility, and transparency of CWS. A one-sided Vickrey–Clarke–Groves (O-VCG) auction mechanism model is designed as the underlying floating billing mechanism to reflect the real-time market value of fine-grained CWS resources. A blockchain-based floating billing prototype system is built as an experimental environment. Our results show that the O-VCG mechanism can effectively reflect the real-time market value of CWSs and increase the revenue of CWS providers. When the supply of CWS providers remains unchanged, allocation efficiency increases when demand increases. By analyzing the performance of the O-VCG auction and comparing it with that of the fixed-rate billing model, the proposed mechanism has more advantages. Moreover, our work provides novel managerial insights for CWS market stakeholders in terms of practical applications.
Consumer sentiment analysis with aspect fusion and <scp>GAN‐BERT</scp> aided adversarial learning

Abstract The sentiment analysis (SA) from the user‐generated reviews is the latest research topic in natural language processing. Nowadays, the extraction of consumer sentiment from the content of the consumer reviews is getting much attention because of its importance in understanding consumers' experiences regarding services or products. Consumer sentiment may be helpful for both consumers and organizations; a consumer can refer to previous consumers' feedback while making their purchase decisions, and organizations can use it in service improvements. For the consumer SA, this article proposed the BERT‐GAN model with review aspect fusion, which improves the fine‐tuning performance of the BERT model by introducing semi‐supervised adversarial learning. For our objective, we extracted various service aspects from consumer reviews and fused them with the word sequences before feeding them into the model. That helps in incorporating aspect representation as well as position information in context with the sentences. Our results analysis and their demonstration show the contribution of the presented model in terms of accuracy compared with the existing models found in the previous work.
Innovation and E-Commerce Models, the Technology Catalysts for Sustainable Development: The Emirate of Dubai Case Study

Innovation and e-commerce models are essential in sustainable development globally. They are among the most important technology and innovation catalysts of Dubai’s pillars. Technologies are essential parts of the strategic approach that aims to become a completely paperless city through digitalisation. The analyses begin with reviewing the most relevant literature on sustainable e-commerce and the e-commerce models. Furthermore, the research identifies the innovation and sustainable paradigms most suitable for developing technologies and e-commerce successfully. Finally, the focus is shifted to the best and most innovative and sustainable e-commerce practices adopted by the companies, contextualised in the Emirate of Dubai as a relevant case study. The primary outcome of this research demonstrated how a thriving environment, marked by adequate investments and opportunities provided by the government, supported e-commerce development and technology adoption. Subsequently, those models are matched with sustainable strategies. The analysis is carried out through an organic growth research framework. The results, therefore, provide valuable insights to multiple stakeholders. Indeed, despite some limitations, the Emirates of Dubai can be considered a benchmark in terms of a strategic digitalisation approach, as its ecosystem proves particularly fruitful for e-commerce development and company sales growth.
Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities

Smart cities are emerging rapidly due to the provisioning of comfort in the human lifestyle. The healthcare system is an important segment of the smart city. The timely delivery of critical human vital signs data to emergency health centers without delay can save human lives. Blockchain is a secure technology that provides the immutable record-keeping of data. Secure data transmission by avoiding erroneous data delivery also demands blockchain technology in healthcare systems of smart cities where patients’ health history is required for their necessary treatments. The health parameter data of each patient are embedded in a separate block in blockchain technology with SHA-256-based cryptography hash values. Mining computing nodes are responsible to find a 32-bit nonce (number only used once) value for each data block to compute a valid SHA-256-based hash value in blockchain technology. Computing nonce for valid hash values is a time-taking process that may cause life losses in the healthcare system. Increasing the mining nodes reduces this delay; however, the uniform distribution of mining data blocks to these nodes by considering the priority data is a challenging task. In this work, an efficient scheme is proposed for scheduling nonce computing tasks at the mining nodes to ensure the timely execution of these tasks. The proposed scheme consists of two parts, the first one provides a load balancing scheme to distribute the nonce execution tasks among the mining nodes such that makespan is minimized and the second part prioritizes more sensitive patient data for quick execution. The results show that the proposed load balancing scheme effectively allocates data blocks in different mining nodes as compared to round-robin and greedy algorithms and computes hash values of most of the higher-risk patients’ data blocks in a reduced amount of time.
Stratification of White Blood Cells Using Optimized DenseNet201 Model

White blood cells, that shape the preface of the safe system, guarantee the body from exterior interlopers and irresistible illnesses. Though the number and essential highlights of WBCs can deliver imperative information nearly the prosperity of people, the extent of the subtypes of these cells and discernible mis-happenings are an extraordinary marker inside the innovative arrangement. The affirmation of cells of the sort of Eosinophil, Lymphocyte, Monocyte and Neutrophil is basic. In this chapter, Significant Learning based CNN models illustrate proposed classification of cell types such as eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The exhibit shown is based on pre-trained models. To choose the predominance of the proposed methodology, the classification was additionally performed and compared utilizing pre-trained DenseNet121, DenseNet201, EfficientNetB0, EfficientNetB7, ResNet50 and ResNet152V2 models individually. The accuracies and losses of these six models are compared in this research and the most excellent result is found on DenseNet201 CNN model which shows with the accuracy of 86% on Adam optimizer utilizing 30 epoch value and 32 batch size. DenseNet201 show gives higher precision than other pre-trained models.
Semantic segmentation-based semantic communication system for image transmission

With the rapid development of artificial intelligence and the widespread use of the Internet of Things, semantic communication, as an emerging communication paradigm, has been attracting great interest. Taking image transmission as an example, from the semantic communication's view, not all pixels in the images are equally important for certain receivers. The existing semantic communication systems directly perform semantic encoding and decoding on the whole image, in which the region of interest cannot be identified. In this paper, we propose a novel semantic communication system for image transmission that can distinguish between regions of interest (ROI) and regions of non-interest (RONI) based on semantic segmentation, where a semantic segmentation algorithm is used to classify each pixel of the image and distinguish ROI and RONI. The system also enables high-quality transmission of ROI with lower communication overheads by transmissions through different semantic communication networks with different bandwidth requirements. An improved metric θPSNR is proposed to evaluate the transmission accuracy of the novel semantic transmission network. Experimental results show that our proposed system achieves a significant performance improvement compared with existing approaches, namely, existing semantic communication approaches and the conventional approach without semantics.
Centralized and distributed adaptive cubature information filters for multi-sensor systems with unknown probability of measurement loss

This paper studies the centralized and distributed state estimation problems for nonlinear multi-sensor systems with unknown probability of measurement loss. Based on the variational Bayesian (VB) method and the cubature information filter (CIF), two adaptive filters, namely centralized adaptive CIF (CA-CIF) and distributed adaptive CIF (DA-CIF), are proposed respectively. First, a series of Bernoulli random variables are introduced as the indicators of measurement loss, and the loss probabilities are modeled as Beta distributions. By transforming all distributions into exponential family form, the CA-CIF can approximate the posterior distributions of the state, loss indicators and loss probabilities by the Expectation-Maximization (EM) algorithm. To reduce the computational cost and improve the reliability, the DA-CIF is further derived by utilizing the average consensus algorithm. Compared with the existing results which rely on the knowledge of the loss indicators or the loss probabilities, the proposed two filters require no information about the measurement loss, and are thus more applicable for practical scenarios. Finally, two examples of target tracking and permanent magnet synchronous motor are included to validate the feasibility and superiority of the proposed two filters.
A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

The traditional method of saving energy in Virtual Machine Placement (VMP) is based on consolidating more virtual machines (VMs) in fewer servers and putting the rest in sleep mode, which may lead to the overheating of servers resulting in performance degradation and cooling cost. The lack of an accurate and computationally efficient model to describe the thermal condition of the data center environment makes it challenging to develop an effective and adaptive VMP mechanism. Although recently, data-driven approaches have acted successfully in model construction, the shortage of clean, adequate, and sufficient amounts of data put limits their generalizability. Moreover, any change in the data center configuration during operation, makes these models prone to error and forces them to repeat the learning process. Thus, researchers turn to applying model-free paradigms such as reinforcement learning. Due to the vast action-state space of real-world applications, scalability is one of the significant challenges in this area. In addition, the delayed feedback of environmental variables such as temperature give rise to exploration costs. In this paper, we present a decentralized implementation of reinforcement learning along with a novel state-action representation to perform the VMP in the data centers to optimize energy consumption and keep the host temperature as low as possible while satisfying Service Level Agreements (SLA). Our experimental results show more than 17% improvement in energy consumption and 12% in CPU temperature reduction compared to baseline algorithms. We also succeeded in accelerating optimal policy convergence after the occurrence of a configuration change.
Multimodal Multi-User Mixed Reality Human–Robot Interface for Remote Operations in Hazardous Environments

In hazardous environments, where conditions present risks for humans, the maintenance and interventions are often done with teleoperated remote systems or mobile robotic manipulators to avoid human exposure to dangers. The increasing need for safe and efficient teleoperation requires advanced environmental awareness and collision avoidance. The up-to-date screen-based 2D or 3D interfaces do not fully allow the operator to immerse in the controlled scenario. This problem can be addressed with the emerging Mixed Reality (MR) technologies with Head-Mounted Devices (HMDs) that offer stereoscopic immersion and interaction with virtual objects. Such human-robot interfaces have not yet been demonstrated in telerobotic interventions in particle physics accelerators. Moreover, robotic operations often require a few experts to collaborate, which increases the system complexity and requires sharing a multi-user Augmented Reality (AR) workspace. The multi-user telerobotics with shared control in the AR has not yet been approached in the industrial state-of-the-art. In this work, the developed MR human-robot interface using the AR HMD is presented. The interface adapts to the constrained wireless networks in particle accelerator facilities and provides reliable high-precision interaction and specialized visualization. The multimodal operation uses hands, eyes and user motion tracking, and voice recognition for control, as well as offers video, 3D point cloud and audio feedback from the robot. Multiple experts can collaborate in the AR workspace locally or remotely, share the robot’s control and monitor robotic teleoperation. Ten (10) operators tested the interface in intervention scenarios in the European Organization for Nuclear Research (CERN) with complete network characterization and measurements to conclude if operational requirements were met and if the network architecture could support single and multi-user communication load. The interface system has proved to be operationally ready at the Technical Readiness Level (TRL) 8 - and was validated through successful tests and demonstration in single and multi-user missions. Some areas of system limitations and further work were identified, such as optimising the network architecture for multi-user scenarios or high-level interface actions applying automatic interaction strategies with the robot depending on network conditions.
A Deep CRNN-Based Sentiment Analysis System with Hybrid BERT Embedding

This paper proposes a novel hybrid embedding to enhance scope of word embeddings by augmenting these with natural language processing operations. We primarily focus on the proposal of new hybrid word embedding generated by augmenting BERT embedding vectors with polarity score. The paper further proposes a new deep learning architecture inspired by the use of convolutional neural network for feature extraction and a bidirectional recurrent network for contextual and temporal feature exploitation. Use of CNN with hybrid embedding allowed the network to extract even the higher-level styles in writing, while bidirectional RNN helped in understanding context. The paper justifies that the proposed architecture and hybrid embedding improves performance of sentiment classification system by performing a large number of experiments and testing on a number of deep learning architectures. The architecture on new hybrid embeddings incurred an accuracy of 96%, which is a significant improvement when compared with recent studies in the literature.
VizOPTICS: Getting insights into OPTICS via interactive visual analysis

Ordering points to identify the clustering structure (OPTICS) is a density-based clustering algorithm that allows the exploration of the cluster structure in the dataset by outputting an ordered queue called cluster ordering. However, this nonexplicit output makes it greatly more difficult for practitioners to identify cluster patterns and obtain high-quality clusters. In this paper, we firstly investigate OPTICS in depth and identify the challenges facing users of OPTICS for cluster analysis through a pilot user study. Then, integrating human intelligence deeply with the machine intelligence of OPTICS, a visual analytics approach, VizOPTICS, is proposed to support practitioners in understanding and applying OPTICS to extract meaningful clustering results. It includes an ordered lattice plot for observing the generation process of cluster ordering, a density scatter plot for analyzing the cluster structure in datasets, and a dynamic reachability plot for optimizing clustering results, and also provides several interaction modes, such as selecting and highlighting, to help users analyze the cluster formation and algorithm operation processes interactively. Finally, we assess our approach through four case studies and a user evaluation study. The results demonstrate the effectiveness and efficiency of the system.
A zero trust and blockchain-based defense model for smart electric vehicle chargers

Electric vehicles (EVs) have rapidly developed over the last decade due to their environmental benefits. As a key component of EVs, electric vehicle chargers are becoming increasingly digital and intelligent. However, due to the vast attack surface and the lack of systematic study, EV chargers and charging management cloud platforms are facing cyber security problems. These problems include weak cryptographic mechanisms, insecure data communication, and malicious firmware attacks. Through specific vulnerabilities, attackers can tamper with the data communication or replay network requests between EV chargers and cloud platforms. It will cause threats such as user-level privacy leakage, power fluctuations in the smart grid, and damage to Electric vehicles, damaging public life and property safety. Given the above, this paper proposes a security protection scheme incorporating blockchain, zero trust, and ShangMi cryptographic (SM) algorithms. The scheme uses Hyperledger Fabric for key management and trust evaluation event storage to guarantee the authenticity, non-repudiation, and tamper-proof of keys and events. In addition, zero trust is applied to secure valuable resources and enforce identity and access management (IAM) for accessing entities. We adopt the dynamic trust evaluation method to assess the trustworthiness of accessing entities in real time to implement dynamic authorization. Furthermore, the SM algorithms SM2, SM3, and SM4 are used to protect data confidentiality, integrity, and authenticity. Experimental results demonstrate that our scheme can effectively resist replay and tampering attacks, securing data communication between EV chargers and cloud platforms. And the performance of the cryptographic algorithm, blockchain system, and Secure Sockets Layer (SSL) meets Chinese national and industry standards.
Parallel incremental association rule mining framework for public opinion analysis

Internet public opinion association rule mining (POARM) has garnered significant attention from a larger group of netizens. However, most POARM methods have been applied to post-event analysis, which has poor timeliness and a low efficiency. Therefore, the real-time monitoring of public opinion association rules is lacking. To address this problem, we propose the parallel Incremental POARM Framework (IPOARM), which improves the timeliness of association rule mining in two ways: 1) using an incremental merge method to consider both inserted and deleted public opinion transaction sets and reuse previous frequent itemsets to reduce redundant computation and 2) employing a parallel implementation of big data process platforms. Moreover, the flexible association rule mining (ARM) algorithm selection structure of IPOARM enables users to freely select suitable ARM algorithms. We represent four classic transaction sets as public opinion transaction sets and compare the IPOARM framework with two novel incremental association rules mining algorithms. Our evaluations indicate that the IPOARM framework can discover Internet public opinion association rules quickly, implying that it can be easily integrated into existing big data processing platforms and that it significantly improves the mining accuracy and efficiency by 12.756% and 29.371%, respectively.
Hybrid System Mixed Reality and Marker-Less Motion Tracking for Sports Rehabilitation of Martial Arts Athletes

Rehabilitation is a vast field of research. Virtual and Augmented Reality represent rapidly emerging technologies that have the potential to support physicians in several medical activities, e.g., diagnosis, surgical training, and rehabilitation, and can also help sports experts analyze athlete movements and performance. In this study, we present the implementation of a hybrid system for the real-time visualization of 3D virtual models of bone segments and other anatomical components on a subject performing critical karate shots and stances. The project is composed of an economic markerless motion tracking device, Microsoft Kinect Azure, that recognizes the subject movements and the position of anatomical joints; an augmented reality headset, Microsoft HoloLens 2, on which the user can visualize the 3D reconstruction of bones and anatomical information; and a terminal computer with a code implemented in Unity Platform. The 3D reconstructed bones are overlapped with the athlete, tracked by the Kinect in real-time, and correctly displayed on the headset. The findings suggest that this system could be a promising technology to monitor martial arts athletes after injuries to support the restoration of their movements and position to rejoin official competitions.
A Unified Interpretable Intelligent Learning Diagnosis Framework for Learning Performance Prediction in Intelligent Tutoring Systems

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners’ current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the abovementioned problem, the proposed unified interpretable intelligent learning diagnosis framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners’ performances compared with the state-of-the-art models and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems.